U.S. patent application number 11/967221 was filed with the patent office on 2009-03-12 for method and system for social network analysis.
This patent application is currently assigned to eBay Inc.. Invention is credited to Zeqian Shen, Neelakantan Sundaresan.
Application Number | 20090070679 11/967221 |
Document ID | / |
Family ID | 40433064 |
Filed Date | 2009-03-12 |
United States Patent
Application |
20090070679 |
Kind Code |
A1 |
Shen; Zeqian ; et
al. |
March 12, 2009 |
METHOD AND SYSTEM FOR SOCIAL NETWORK ANALYSIS
Abstract
Methods and system for social network analysis are described. In
one embodiment, user interaction data of a time period for a
plurality of users in a social network may be accessed. Network
analysis may be performed on the user interaction data. A
necktie-shaped graph may be generated from the user interaction
data in accordance with the performing of the network analysis. The
necktie-shaped graph may be utilized for analysis of the social
network.
Inventors: |
Shen; Zeqian; (Alameda,
CA) ; Sundaresan; Neelakantan; (Mountain View,
CA) |
Correspondence
Address: |
SCHWEGMAN, LUNDBERG & WOESSNER, P.A.
P.O. BOX 2938
MINNEAPOLIS
MN
55402
US
|
Assignee: |
eBay Inc.
San Jose
CA
|
Family ID: |
40433064 |
Appl. No.: |
11/967221 |
Filed: |
December 30, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60971904 |
Sep 12, 2007 |
|
|
|
60984677 |
Nov 1, 2007 |
|
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Current U.S.
Class: |
715/733 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 30/0601 20130101; G06Q 50/01 20130101 |
Class at
Publication: |
715/733 |
International
Class: |
G06F 3/00 20060101
G06F003/00 |
Claims
1. A method comprising: accessing user interaction data of a time
period for a plurality of users in a social network; performing
network analysis on the user interaction data; generating a
necktie-shaped graph from the user interaction data in accordance
with the performing of the network analysis; and utilizing the
necktie-shaped graph for analysis of the social network.
2. The method of claim 1, further comprising: accessing reputation
information associated with the plurality of users; and applying a
texture to the necktie-shaped graph in accordance with the
reputation information.
3. The method of claim 1, further comprising: accessing interaction
frequency data associated with the plurality of users; and applying
a texture to the necktie-shaped graph in accordance with the
interaction frequency data.
4. The method of claim 1, further comprising: accessing
transactional financial data associated with the plurality of
users; and applying a texture to the necktie-shaped graph in
accordance with the transactional financial data.
5. The method of claim 1, wherein the generating comprises:
generating a strongly connected component of the necktie-shaped
graph in accordance with the performing of the network analysis;
generating an in-component of the necktie-shaped graph in
accordance with the performing of the network analysis; and
generating an out-component of the necktie-shaped graph in
accordance with the performing of the network analysis; and using a
tube to connect the in-component to the out-component, wherein the
in-component of the necktie-shaped graph is smaller than the
out-component of the necktie-shaped graph.
6. The method of claim 5, further comprising: generating one or
more tendrils in accordance with the performing of the network
analysis, a particular tendril of the one or more tendrils
connected to the in-component or the out-component.
7. The method of claim 5, further comprising: generating a
disconnected part in accordance with the performing of the network
analysis, the disconnected part being disconnected from the
strongly connect component, the in-component, and the out-component
in the necktie-shaped graph.
8. The method of claim 1, wherein the utilizing comprises:
analyzing the necktie-shaped graph; making a decision regarding the
social network in accordance with the analyzing of the
necktie-shaped graph; and altering an aspect of the social network
in accordance with the making of the decision.
9. The method of claim 1, wherein the utilizing comprises:
providing the necktie-shaped graph for presentation.
10. The method of claim 1, wherein the utilizing comprises:
accessing additional user interaction data associated with the
social network during a different time period; performing the
network analysis on the additional user interaction data;
generating an additional necktie-shaped graph from the additional
user interaction data in accordance with the performing of the
network analysis; and using the necktie-shaped graph and the
additional necktie-shaped graph for analysis of the social
network.
11. The method of claim 10, wherein the using comprises: providing
a difference between the necktie-shaped graph and the additional
necktie-shaped graph for presentation.
12. The method of claim 1, wherein the user interaction data is
associated with a single transaction category.
13. A method comprising: accessing user interaction data associated
for a plurality of users for a time period in a social network;
performing network analysis on the user interaction data; selecting
a plurality of example users within the social network, each of the
example users being associated with reputation information;
generating a motif for the plurality of example users for the time
period in accordance with the performing of the network analysis, a
node of the motif being associated with a particular example user
of the example users, the motif defining an expected relationship
between the plurality of example users in the social network;
distinguishing the node of the plurality of example users in
accordance with the reputation information of a respective example
user; and utilizing the motif with a plurality of distinguished
nodes for analysis of the social network.
14. The method of claim 13, wherein the utilizing comprises:
providing the motif with the plurality of distinguished nodes for
presentation.
15. The method of claim 13, wherein the utilizing comprises:
analyzing a template including the motif and a plurality of
additional motifs; and making a decision regarding the social
network in accordance with the analyzing of the template.
16. The method of claim 13, further comprising: accessing
interaction frequency data associated with the plurality of users;
and applying a texture to at least one connecting line of the motif
in accordance with the interaction frequency data.
17. The method of claim 13, further comprising: accessing
transactional financial data associated with the plurality of
users; and applying a texture to at least one connecting line of
the motif in accordance with the transactional financial data.
18. The method of claim 13, wherein the distinguishing comprises:
colorizing the node of the plurality of example users in accordance
with the reputation information.
19. The method of claim 13, wherein the social network is a social
commerce network.
20. A machine-readable medium comprising instructions, which when
implemented by one or more processors perform the following
operations: access user interaction data of a time period for a
plurality of users in a social network; perform network analysis on
the user interaction data; generate a necktie-shaped graph from the
user interaction data in accordance with the performing of the
network analysis; and utilize the necktie-shaped graph for analysis
of the social network.
21. The machine-readable medium of claim 20, wherein the one or
more operations to generate the necktie-shaped graph include:
generate a strongly connected component of the necktie-shaped graph
in accordance with the performing of the network analysis; generate
an in-component of the necktie-shaped graph in accordance with the
performing of the network analysis; and generate an out-component
of the necktie-shaped graph in accordance with the performing of
the network analysis; and use a tube to connect the in-component to
the out-component; wherein the in-component of the necktie-shaped
graph is smaller than the out-component of the necktie-shaped
graph.
22. A machine-readable medium comprising instructions, which when
implemented by one or more processors perform the following
operations: access user interaction data associated for a plurality
of users for a time period in a social network; perform network
analysis on the user interaction data; select a plurality of
example users within the social network, each of the example users
being associated with reputation information; generate a motif for
the plurality of example users for the time period in accordance
with the performing of the network analysis, a node of the motif
being associated with a particular example user of the example
users, the motif defining an expected relationship between the
plurality of example users in the social network; distinguish the
node of the plurality of example users in accordance with the
reputation information of a respective example user; and utilize
the motif with a plurality of distinguished nodes for analysis of
the social network.
23. The machine-readable medium of claim 22 further comprising
instructions, which when implemented by one or more processors
perform the following operations: access interaction frequency data
associated with the plurality of users; and apply a texture to at
least one connecting line of the motif in accordance with the
interaction frequency data.
24. A system comprising: a transaction relationship data access
module to access user interaction data of a time period for a
plurality of users in a social network; a network analysis
performance module to perform network analysis on the user
interaction data accessed by the transaction relationship data
access module; a graph generation module to generate a
necktie-shaped graph from the user interaction data in accordance
with the performing of the network analysis by the network analysis
performance module; and a graph provider module to provide the
necktie-shaped graph generated by the graph generation module for
presentation.
25. The system of claim 24, further comprising: a reputation
information access module to access reputation information
associated with the plurality of users; and a texture application
module to apply a texture to the necktie-shaped graph generated by
the graph generation module in accordance with the reputation
information.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of United States
Provisional Patent Applications entitled "Social Network Analysis",
Ser. No. 60/971,904, filed Sep. 12, 2007 and entitled "Analysis of
a Social Commerce Network", Ser. No.: 60/984,677, filed Nov. 1,
2007, the entire contents of which are herein incorporated by
reference.
BACKGROUND
[0002] The web is evolving from a content and commerce space to a
space of social interactions. With the space of social
interactions, users may interact with one another in both
commercial settings and non-commercial settings (e.g., an
information only exchange).
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings in
which:
[0004] FIG. 1 is a block diagram of a system, according to an
example embodiment;
[0005] FIG. 2 is a block diagram of an example graphing subsystem
that may be deployed within the system of FIG. 1 according to an
example embodiment;
[0006] FIG. 3 is a block diagram of an example social strength
subsystem that may be deployed within the system of FIG. 1
according to an example embodiment;
[0007] FIG. 4 is a block diagram of an example motif subsystem that
may be deployed within the system of FIG. 1 according to an example
embodiment;
[0008] FIG. 5 is a block diagram of an example plotting subsystem
that may be deployed within the system of FIG. 1 according to an
example embodiment;
[0009] FIG. 6 is a flowchart illustrating a method for graphing
according to an example embodiment;
[0010] FIG. 7 is a flowchart illustrating a method for graph
generation according to an example embodiment;
[0011] FIGS. 8A-8E are block diagrams of graphs according to an
example embodiment;
[0012] FIG. 9 is a block diagram of a table according to an example
embodiment;
[0013] FIGS. 10 and 11 are flowcharts illustrating a method for
graph utilization according to an example embodiment;
[0014] FIG. 12 is a flowchart illustrating a method for graph usage
according to an example embodiment;
[0015] FIG. 13 is a flowchart illustrating a method for conducting
social strength analysis according to an example embodiment;
[0016] FIG. 14 is a flowchart illustrating a method for accessing
social network values according to an example embodiment;
[0017] FIG. 15 is a flowchart illustrating a method for social
strength utilization according to an example embodiment;
[0018] FIGS. 16 and 17 are block diagrams of charts according to an
example embodiment;
[0019] FIG. 18 is a flowchart illustrating a method for conducting
social strength analysis according to an example embodiment;
[0020] FIG. 19 is a flowchart illustrating a method for conducting
motif analysis according to an example embodiment;
[0021] FIG. 20 is a flowchart illustrating a method for motif
utilization according to an example embodiment;
[0022] FIG. 21 is a block diagram of an example motif display
according to an example embodiment;
[0023] FIG. 22 is a flowchart illustrating a method for
differentiated plotting analysis according to an example
embodiment;
[0024] FIG. 23 is a flowchart illustrating a method for
differentiated plotting utilization according to an example
embodiment;
[0025] FIGS. 24-26 are diagrams of example differentiated plottings
according to an example embodiment;
[0026] FIG. 27 is a network diagram depicting a network system,
according to one embodiment, having a client server architecture
configured for exchanging data over a network;
[0027] FIG. 28 is a block diagram illustrating an example
embodiment of multiple network and marketplace applications, which
are provided as part of the network-based marketplace; and
[0028] FIG. 29 is a block diagram diagrammatic representation of
machine in the example form of a computer system within which a set
of instructions for causing the machine to perform any one or more
of the methodologies discussed herein may be executed.
DETAILED DESCRIPTION
[0029] Example methods and systems for social network analysis are
described. In the following description, for purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of example embodiments. It will be
evident, however, to one skilled in the art that the present
invention may be practiced without these specific details.
[0030] In an example embodiment, user interaction data of a time
period for a plurality of users in a social network may be
accessed. Network analysis may be performed on the user interaction
data. A necktie-shaped graph may be generated from the user
interaction data in accordance with the performing of the network
analysis. The necktie-shaped graph may be utilized for analysis of
the social network.
[0031] In an example embodiment, a strongly connected component
value, an in-component value, an out-component value, a
disconnected component value, a tendril value, and a tube value of
a social network for a time period may be accessed. A social
strength of the social network for the time period may be
calculated by combining the strongly connected component value, the
in-component value, the out-component value, the disconnected
component value, the tendril value, and the tube value. The social
strength of the social network for the time period may be utilized
for analysis of the social network. The strongly connected
component value may have a greatest weight and the disconnected
component value may have the lowest weight in the combining.
[0032] In an example embodiment, a strongly connected component
value, an in-component value, an out-component value, a
disconnected component value, a tendril value, and a tube value of
a social network for a time period may be accessed. A social
strength of the social network for the time period may be
calculated by combining the strongly connected component value, the
in-component value, the out-component value, the disconnected
component value, the tendril value, and the tube value. One or more
users associated with the strongly connected component may be
identified. The strongly connected component value may be a value
of the strongly connected component for the time period. An aspect
of the social network associated with the one or more users may be
modified. The strongly connected component value, the in-component
value, the out-component value, the disconnected component value,
the tendril value, and the tube value of the social network for an
additional time period may be accessed. The additional time period
may be after the modifying of the aspect. The social strength of
the social network for the additional time period may be calculated
by combining the strongly connected component value, the
in-component value, the out-component value, the disconnected
component value, the tendril value, and the tube value. The social
strength of the social network for the time period and the
additional time period may be utilized for analysis in accordance
with the modifying of the aspect of the social network.
[0033] In an example embodiment, user interaction data associated
for a plurality of users for a time period in a social network may
be accessed. Network analysis may be performed on the user
interaction data. A plurality of example users within the social
network may be selected. Each of the example users may be
associated with reputation information. A motif may be generated
for the plurality of example users for the time period in
accordance with the performing of the network analysis. A node of
the motif may be associated with a particular example user of the
example users. The motif may define an expected relationship
between the plurality of example users in the social network. The
node of the plurality of example users may be distinguished in
accordance with the reputation information of a respective example
user. The motif with a plurality of distinguished nodes may be
utilized for analysis of the social network.
[0034] In an example embodiment, reputation information associated
with a plurality of initiating users and a plurality of responding
users in a social network for a time period may be accessed.
Interaction frequency data associated with the plurality of
initiating users and the plurality of responding users for the time
period may be accessed. An aggregated correlation between the
plurality of initiating users and the plurality of responding users
may be plotted in accordance with the reputation information. The
plotting of the aggregated correlation may be differentiated in
accordance with the interaction frequency data. The differentiated
plotting of the aggregated correlation may be utilized.
[0035] FIG. 1 illustrates an example system 100 in which a
community of users may use a number of client machines 102.1-102.n
to be involved in a social network. The client machine 102 may be a
computing system, mobile phone, a personal digital assistant (PDA),
a gaming unit, a portable computing unit, and the like. The social
network may be a social commerce network over with the users
operating the machines may be involved in commercial exchange
(e.g., buying or selling). However, other types of social networks
(e.g., informational social networks) may also be used.
[0036] In an example embodiment, the social network may be a social
structure made of nodes (e.g., individuals or organizations) that
are tied by one or more specific types of interdependency
including, by way of example, values, visions, idea, commerce,
friends, kinship, dislike, conflict, web links, sexual relations,
disease transmission, or airline routes. For example, a social
commerce network may be a network that includes a commercial
interdependency.
[0037] The client machines 102.1-102.n may participate in the
social network by communicating over a provider network 104 with a
network analyzer 106. The provider network 104 may be a Global
System for Mobile Communications (GSM) network, an Internet
Protocol (IP) network, a Wireless Application Protocol (WAP)
network, a WiFi network, or a IEEE 802.11 standards network as well
as various combinations thereof. Other conventional and/or later
developed wired and wireless networks may also be used.
[0038] The network analyzer 106 may enable the social network to be
provided to the users of the client machines 102.1-102.n. The
network analyzer 106 may be used to analyze the social network by
using a graphing subsystem 108, a social strength subsystem 110, a
motif subsystem 112, and/or a plotting subsystem 114. Example
embodiments of the subsystems 108-114 are described in greater
detail below.
[0039] FIG. 2 is an example of a graphing subsystem 108 that may be
deployed in the network analyzer 106 of the system 100 (see FIG. 1)
or another system according to an example embodiment.
[0040] The graphing subsystem 108 may include a user interaction
data access module 202, a network analysis performance module 204,
a graph generation module 206, a graph utilization module 208, a
reputation information access module 210, a interaction frequency
data access module 212, a transactional financial data access
module 214, a texture application module 216, a graph analysis
module 218, a shape change measurement module 220, a decision
making module 222, a network alteration module 224, a graph
provider module 226, an aspect alteration module 228, and/or a
difference provider module 230. Other modules may also be used.
[0041] The user interaction data access module 202 accesses user
interaction data of a time period for a number of users in a social
network and/or accesses additional user interaction data associated
with the social network during a different time period.
[0042] The network analysis performance module 204 performs network
analysis on user interaction data and/or additional user
interaction data. The user interaction data may be based on
communications between users. For example, the user interaction
data may include, by way of example, transactional relationship
data that relates to a transaction (e.g., a sale or item exchange)
that has occurred between users, communication interaction data
that relates to a communication (e.g., an e-mail, an instant
message, or a voice over IP call) that has occurred between users,
and the like.
[0043] The graph generation module 206 generates a graph (e.g., a
necktie-shaped graph) from the user interaction data and/or an
additional graph from the additional user interaction data in
accordance with the performing of the network analysis.
[0044] The graph utilization module 208 uses a graph and/or an
additional graph for analysis of the social network. The reputation
information access module 210 accesses reputation information
associated with the number of users. The reputation information may
include, by way of example, user feedback (e.g., as provided by
eBay Inc., of San Jose, Calif.), a rating of a posting, or the
like.
[0045] The interaction frequency data access module 212 accesses
interaction frequency data associated with the number of users. The
transactional financial data access module 214 accesses
transactional financial data associated with the number of
users.
[0046] The texture application module 216 applies a texture to the
graph in accordance with reputation information, interaction
frequency data and/or transactional financial data. The graph
analysis module 218 analyzes the graph.
[0047] The shape change measurement module 220 measures a shape
change between the graph and the additional graph. The decision
making module 222 makes a decision regarding the social network in
accordance with the analyzing of the graph and/or the measuring of
the shape change.
[0048] The network alteration module 224 alters an aspect of the
social network in accordance with the making of the decision. The
graph provider module 226 provides the graph and/or the additional
graph for presentation.
[0049] The aspect alteration module 228 alters an aspect of the
social network in accordance with the making of the decision. The
difference provider module 230 provides a difference between the
graph and the additional graph for presentation.
[0050] FIG. 3 is an example of a social strength subsystem 110 that
may be deployed in the network analyzer 106 of the system 100 (see
FIG. 1) or another system according to an example embodiment.
[0051] The social strength subsystem 110 may include a user
identification module 302, an aspect modification module 304, a
value access module 306, a social strength calculation module 308,
a social strength provider module 310, a social strength
utilization module 312, and/or a difference provider module 314.
Other modules may also be used.
[0052] The user identification module 302 identifies one or more
users associated with the strongly connected component. The
strongly connected component value may be a value of the strongly
connected component for the time period. The aspect modification
module 304 modifies an aspect of the social network associated with
the one or more users.
[0053] The value access module 306 accesses a strongly connected
component value, an in-component value, an out-component value, a
disconnected component value, a tendril value, and a tube value of
a social network for a time period and/or an additional time
period.
[0054] The social strength calculation module 308 calculates a
social strength of the social network or the social strength of the
social network for the categories for the time period and/or an
addition time period by combining the strongly connected component
value, the in-component value, the out-component value, the
disconnected component value, the tendril value, and the tube
value. The combination may be by a linear combination, a quadratic
equation, or the like.
[0055] The social strength provider module 310 provides the social
strength of the social network and/or one or more categories in the
social network for the time period and/or an addition time period
for presentation. The social strength utilization module 312 uses
the social strength of the social network and/or for a number of
categories of the social network for the time period and/or the
additional time period for analysis of the social network.
[0056] The difference provider module 314 provides a difference
between the social strength of the social network for the time
period and the additional time period for presentation.
[0057] FIG. 4 is an example of a motif subsystem 112 that may be
deployed in the network analyzer 106 of the system 100 (see FIG. 1)
or another system according to an example embodiment.
[0058] The motif subsystem 112 may include a data access module
402, a network analysis performance module 404, an example user
selection module 406, a motif generation module 408, a node
distinguishing module 410, a texture application module 412, a
motif provider module 414, a template analysis module 416, and/or a
decision making module 418. Other modules may also be used.
[0059] The data access module 402 accesses user interaction data,
interaction frequency data, and/or transactional financial data
associated for a number of users for a time period in a social
network.
[0060] The network analysis performance module 404 performs network
analysis on the user interaction data. The example user selection
module 406 selects a number of example users within the social
network, each of the example users being associated with reputation
information.
[0061] The motif generation module 408 generates a motif for the
number of example users for the time period in accordance with the
performing of the network analysis. The node distinguishing module
410 distinguishes a node of the example users in accordance with
the reputation information of a respective example user.
[0062] The texture application module 412 applies a texture to at
least one connecting line of the motif in accordance with the
interaction frequency data and/or the transactional financial data.
The interaction frequency data may include the number of times with
which users interacted with one another.
[0063] The motif provider module 414 provides the motif with the
number of distinguished nodes for presentation. The template
analysis module 416 analyzes a template including the motif and a
number of additional motifs. The decision making module 418 makes a
decision regarding the social network in accordance with the
analyzing of the template.
[0064] FIG. 5 is an example of a plotting subsystem 500 that may be
deployed in the network analyzer 106 of the system 100 (see FIG. 1)
or another system according to an example embodiment.
[0065] The plotting subsystem 500 may include a reputation
information access module 502, a interaction frequency data access
module 504, an aggregated correlation plotting module 506, a
plotting differentiation module 508, a plotting provider module
510, and/or a plotting utilization module 512. Other modules may
also be used.
[0066] The reputation information access module 502 accesses
reputation information associated with initiating users (e.g.,
buyers) and responding users (e.g., sellers) in a social network
for a time period and/or an additional time period.
[0067] The interaction frequency data access module 504 accesses
interaction frequency data associated with the initiating users and
the responding users for the time period and/or the additional time
period.
[0068] The aggregated correlation plotting module 506 plots an
aggregated correlation between the initiating users and the
receiving users in accordance with the reputation information
and/or the assorted initiating users and the assorted initiating
users in accordance with the reputation information. The assorted
initiating users may include one or more of the initiating users.
The assorted receiving users may include one or more of the
receiving users.
[0069] The plotting differentiation module 508 differentiates the
plotting of the aggregated correlation in accordance with the
interaction frequency data. The plotting provider module 510
provides the differentiated plotting of the aggregated correlation
for presentation. The plotting utilization module 512 uses the
differentiated plotting of the aggregated correlation for the time
period and the additional time period for the analysis of the
social network.
[0070] Necktie-Shape Graphing
[0071] FIG. 6 illustrates a method 600 for graphing according to an
example embodiment. The method 600 may be performed by the network
analyzer 106 (see FIG. 1) in the system 100 or another system.
[0072] User interaction data of a time period for users in a social
network (e.g., a social commerce network) is accessed at block 602.
The user interaction data may be associated with a single
transaction category or multiple transaction categories.
[0073] Network analysis is performed on the user interaction data
at block 604. A graph is generated from the user interaction data
in accordance with the performing of the network analysis at block
606. The graph may be a necktie-shaped graph, a bowtie shaped
graph, or be in a different shape. In addition, the graphs may have
different sized dimensions based on a particular
representation.
[0074] At decision block 608, a determination may be made whether
to apply a texture to the graph. If a determination is made to
apply a texture, additional user data may be accessed at block 610
and a textured may be applied to the graph in accordance with the
additional user data at block 612. The additional user data may
include, by way of example, interaction frequency data, reputation
information, transactional financial data, or other data associated
with the users.
[0075] The texture applied to the graph may include colorization,
striping, and the like. The texture may better enable a user to
more easily understand more about the relationship of the users
reflected in the graph. For example, users in a particular
component of the graph may be making a large number of interactions
or a small number of interactions.
[0076] If a determination is made at decision block 608 not to
apply a texture or upon completion of the operations at block 612,
the method 600 may proceed to block 614.
[0077] The graph is utilized for analysis of the social network at
block 614. For example, the graph may be provided for
presentation.
[0078] FIG. 7 illustrates a method 700 for graph generation
according to an example embodiment. The method 700 may be performed
at block 604 or otherwise performed.
[0079] A strongly connected component of the graph is generated in
accordance with the performing of the network analysis at block
702. An in-component of the graph is generated in accordance with
the performing of the network analysis at block 704.
[0080] An out-component of the graph is generated in accordance
with the performing of the network analysis at block 706. The
in-component of the graph may be smaller than the out-component of
the graph.
[0081] At block 708, a tube is used to connect the in-component to
the out-component. One or more tendrils may be generated in
accordance with the performing of the network analysis at block
710. The one or more tendrils may be connected to the in-component
or the out-component.
[0082] A disconnected part may be generated in accordance with the
performing of the network analysis at block 712. The disconnected
part may be disconnected from the strongly connect component, the
in-component, and the out-component in the graph.
[0083] FIG. 8A is a diagram of an example necktie-shaped graph 800
according to an example embodiment. The necktie-shaped graph 800 is
an example representation of a graph that may be generated in
accordance with the method 700 (see FIG. 7). However, other
representations of the same or different types of graphs may also
be generated.
[0084] A strongly connected component 802 may be connected to an
in-component 804 and an out-component 806. While the representation
of the example necktie-shaped graph 800 reflects a strongly
connected component value of 5.83%, an in-component value of 3.03%,
and an out-component value of 65.83%, other values may be used in
other representations.
[0085] The in-component 804 and the out-component 806 may be
connected through a tube 808. The tube value in the presentation
representation is 0.64%, however other values may be used in other
representations.
[0086] One or more tendrils 810.1-810.6 may be connected to the
in-component 804 or the out-component 806. However, a different
number of tendrils 810.1-810.6 may be used in a different
representation. The tendril value in the presentation
representation is 23.59%, however other values may be used in other
representations.
[0087] A disconnected component 812 may not be connected to the
strongly connected component 802, the in-component 804, the
out-component 806, the tube 808, and/or the tendrils 810.1-810.6.
The disconnected component value in the presentation representation
is 1.09%, however other values may be used in other
representations.
[0088] FIGS. 8B-8E are diagrams of example graphs 820, 840, 860,
880 according to example embodiments. Each of the graphs 820, 840,
860, 880 include a strongly connected component, an in-component,
an out-component, a tube, one or more tendrils, and a disconnected
component. The graph 820 may represent an Antiques category, the
graph 840 may represent a Collectibles category, the graph 860 may
represent a Sports Memorabilia and Cards category, and the graph
880 may represent a Stamps category.
[0089] FIG. 9 illustrates a block diagram of an example table 900
according to an example embodiment. The table 900 is an example
representation that may reflect a distribution of various
components of a graph (e.g., the necktie-shaped graph 800 of FIG.
8). However, other representations containing different values
and/or components may also be used.
[0090] The size row 902 of the table 900 may reflect a size
percentage of various components of a graph. The average feedback
row 904 may contain values that reflect an average feedback store
of users associated with a particular component. The purchases row
906 may contain values that reflect an average and percentage of
purchases associated with a particular component. The sales row 908
may contain values that reflect an average and percentage of sales
associated with a particular component. The columns 910-029 are
associated with particular components of a graph.
[0091] FIG. 10 illustrates a method 1000 for graph utilization
according to an example embodiment. The method 1000 may be
performed at block 614 or otherwise performed.
[0092] The graph is analyzed at block 1002. A decision regarding
the social network is made in accordance with the analyzing of the
graph at block 1004. For example, an area of weakness may be
identified in the social network.
[0093] At block 1006, an aspect of the social network is altered in
accordance with the making of the decision.
[0094] FIG. 11 illustrates a method 1100 for graph utilization
according to an example embodiment. The method 1100 may be
performed at block 614 or otherwise performed.
[0095] At block 1102, additional user interaction data associated
with the social network is accessed during a different time period.
Network analysis is performed on the additional user interaction
data at block 1104.
[0096] An additional graph is generated from the additional user
interaction data in accordance with the performing of the network
analysis at block 1106.
[0097] At block 1108, the graph and the additional graph are used
for analysis of the social network. For example, the graph and the
additional graph may be provided for presentation and/or a
difference between the graph and the additional graph may be
provided for presentation.
[0098] FIG. 12 illustrates a method 1200 for graph usage according
to an example embodiment. The method 1200 may be performed at block
1108 or otherwise performed.
[0099] A shape change between the graph and the additional graph is
measured at block 1202. A decision regarding the social network is
made in accordance with the measuring of the shape change at block
1204. For example, the decision may include providing one or more
users with an incentive to become associated with a component, to
exclude users from the social network, or the like. An aspect of
the social network is altered in accordance with the making of the
decision at block 1206.
[0100] Social Strength Analysis
[0101] FIG. 13 illustrates a method 1300 for conducting social
strength analysis according to an example embodiment. The method
1300 may be performed by the network analyzer 106 (see FIG. 1) in
the system 100 or another system.
[0102] A strongly connected component value, an in-component value,
an out-component value, a disconnected component value, a tendril
value, and/or a tube value of a social network (e.g., a social
commerce network) for a time period is accessed at block 1302.
[0103] At block 1304, social strength of the social network is
calculated for the time period by combining the strongly connected
component value, the in-component value, the out-component value,
the disconnected component value, the tendril value, and/or the
tube value. The social strength may be calculated for the entire
social network and/or a number of categories in the social network
for the time period. The combination may be through a linear
combination or a different type of function.
[0104] The strongly connected component value may have greatest
weight in the combination. The disconnected component value may
have the lowest weight in the combination. The weight of the
in-component, the out-component, and the tube may be equally
weighted. For example, the weight of the strongly connected
component value may be double the weight of the in-component and
the out-component and the weight of the disconnected component
value may be half the weight of the in-component and the
out-component in the linear combination. However, other weightings
in the combination may also be used.
[0105] The social strength of the social network for the time
period is utilized for analysis of the social network at block
1306. For example, the social strength of the social network for
the time period may be provided for presentation.
[0106] FIG. 14 illustrates a method 1400 for accessing social
network values according to an example embodiment. The method 1400
may be performed at block 614, block 1302, or otherwise
performed.
[0107] A strongly connected component value is determined in
accordance with a graph percentage of a strongly connected
component of the social network at block 1402.
[0108] An in-component value is determined in accordance with the
graph percentage of an in-component of the social network at block
1404. An out-component value is determined in accordance with the
graph percentage of an out-component of the social network at block
1406. A disconnected component value may be determined in
accordance with the graph percentage of a disconnected component of
the social network at block 1408.
[0109] A tendril value may be determined in accordance with the
graph percentage of one or more tendrils of the social network at
block 1410. A tube value may be determined in accordance with the
graph percentage of a tube of the social network at block 1412.
[0110] FIG. 15 illustrates a method 1500 for accessing social
strength utilization according to an example embodiment. The method
1500 may be performed at block 1306 or otherwise performed.
[0111] The strongly connected component value, the in-component
value, the out-component value, the disconnected component value,
the tendril value, and/or the tube value of the social network is
accessed for an additional time period at block 1502.
[0112] At block 1504, the social strength of the social network is
calculated for the additional time period by taking the linear
combination of the strongly connected component value, the
in-component value, the out-component value, the disconnected
component value, the tendril value, and/or the tube value. The
social strength may be calculated for the entire social network
and/or a number of categories in the social network for the
additional time period.
[0113] The social strength of the social network for the time
period and the additional time period is used for analysis of the
social network at block 1506. For example, the social strength of
the social network for the time period and the additional time
period and/or a difference between the social strength of the
social network for the time period and the additional time period
may be provided for presentation. The provided social strength may
be for one or more categories of the social network or the entire
social network.
[0114] FIG. 16 is a block diagram of a chart 1600 according to an
example embodiment. The chart 1600 is an example comparison of the
network shapes of multiple categories of an example social network.
For example, the categories reflected in the social network of the
chart 1600 include an entire network, Antiques, Art, Baby, Books,
Business & Industrial, Cameras & Photo, Clothing, Shoes
& Apparel, Collectibles, Computers & Networking, Consumer
Electronics, Crafts, Dolls & Bears, DVDs & Movies,
Entertainment Memorabilia, Everything Else, Gift Certificates,
Health & Beauty, Home & Garden, Jewelry & Watches, Live
Auctions, Music, Musical Instruments, Pottery & Glass, Real
Estate, Specialty Services, Sporting Goods, Sports Memorabilia
& Cards, Stamps, Tickets, Toys & Hobbies, Travel, and Video
Games. Other social networks may be categorized with a different
number of categories and/or different types of categories.
[0115] FIG. 17 is a block diagram of a chart 1700 according to an
example embodiment. The chart 1700 is an example comparison of the
social strength of multiple categories in a social network.
However, other comparisons may also be used. For example, other
social networks may be categorized with a different number of
categories and/or different types of categories.
[0116] FIG. 18 illustrates a method 1800 for conducting social
strength analysis according to an example embodiment. The method
1800 may be performed by the network analyzer 106 (see FIG. 1) in
the system 100 or another system.
[0117] A strongly connected component value, an in-component value,
an out-component value, a disconnected component value, a tendril
value, and/or a tube value of a social network for a time period is
accessed at block 1802.
[0118] At block 1804, a social strength of the social network for
the time period is calculated by taking a linear combination of the
strongly connected component value (e.g., a value of a strongly
connected component), the in-component value, the out-component
value, the disconnected component value, the tendril value, and the
tube value.
[0119] One or more users associated with the strongly connected
component are identified at block 1806. An aspect of the social
network associated with the one or more users may be modified at
block 1808. For example, the one or more users may be provided with
an incentive to have a number of other users utilize a feature of
the social network and/or with a designated status in the social
network. Other aspects of the social network may also be
modified.
[0120] At block 1810, the strongly connected component value, the
in-component value, the out-component value, the disconnected
component value, the tendril value, and the tube value of the
social network may be accessed for an additional time period. The
additional time period may be after the modifying of the aspect
performed at the block 1808.
[0121] At block 1812, the social strength of the social network is
calculated for the additional time period by taking the linear
combination of the strongly connected component value, the
in-component value, the out-component value, the disconnected
component value, the tendril value, and/or the tube value.
[0122] The social strength of the social network for the time
period and the additional time period is used for analysis at block
1814.
[0123] Motifs
[0124] FIG. 19 illustrates a method 1900 for conducting motif
analysis according to an example embodiment. The method 1900 may be
performed by the network analyzer 106 (see FIG. 1) in the system
100 or another system.
[0125] User interaction data associated for users for a time period
in a social network (e.g., a social commerce network) is accessed
at block 1902. Network analysis is performed on the user
interaction data at block 1904.
[0126] Example users within the social network are selected at
block 1906. The example users may be associated with reputation
information (e.g., user feedback).
[0127] A motif for the example users for the time period is
generated in accordance with the performing of the network analysis
at block 1908. A node of the motif may be associated with an
example user. The motif may define an expected relationship between
a number of example users in the social network. For example, a
four node motif may be generated.
[0128] The node of the example users may be distinguished in
accordance with the reputation information of a respective example
user at block 1910. For example, the node of the example users may
be colored in accordance with the reputation information.
[0129] At decision block 1912, a determination may be made whether
to apply a texture to the motif. If a determination is made to
apply a texture, additional user data may be accessed at block 1914
and the texture may be applied to one or more connected lines of
the motif in accordance with the additional user data at block
1916. For example, the additional user data may include interaction
frequency data and/or transaction financial data associated with
the users. If a determination is made not to apply the texture at
decision block 1912 or upon completion of the operations at block
1916, the method 1900 may proceed to the block 1918.
[0130] The motif with the distinguished nodes may be utilized for
analysis of the social network at block 1918. For example, the
motif with the distinguished nodes may be provided for
presentation.
[0131] FIG. 20 illustrates a method 2000 for motif utilization
according to an example embodiment. The method 2000 may be
performed at block 1918 or otherwise performed.
[0132] The method 2000 may be performed at block 1918 or otherwise
performed. A template including the motif and a number of
additional motifs is analyzed at block 2002.
[0133] A decision regarding the social network is made in
accordance with the analyzing of the template at block 2004. At
block 2006, at least one aspect of the social network is altered in
accordance with the making of the decision.
[0134] FIG. 21 is a block diagram of example motif display 2100
according to an example embodiment. The motif display 2100 is an
example representation of four node motifs from two categories of a
social network. However, motifs may be made for other categories of
the social network or the entire social network. Motifs containing
a different number of nodes may also be used.
[0135] The motif display 2100 includes a number of motifs 2126-2144
for a first category 2102 and a number of motifs 2146 for a second
category 2164.
[0136] A distinguishing legend 2106 may include a series of
distinguishing levels 2180-2124 that reflect different reputation
information associated with users of the motifs 2126-2164. For
example, the nodes of the motif 2126 includes a first node with a
distinguishing level 2118, a second node with a distinguishing
level 2120, a third node with a distinguishing level 2122, and a
fourth node with a distinguishing level 2124.
[0137] Differentiated Plotting
[0138] FIG. 22 illustrates a method 2200 for differentiated
plotting analysis according to an example embodiment. The method
2200 may be performed by the network analyzer 106 (see FIG. 1) in
the system 100 or another system.
[0139] Reputation information associated with a number of
initiating users and a number of responding users in a social
network for a time period is accessed at block 2202.
[0140] Interaction frequency data associated with the initiating
users and the responding users for the time period is accessed at
block 2204. An aggregated correlation between the initiating users
and the responding users is plotted in accordance with the
reputation information at block 2206.
[0141] The plotting of the aggregated correlation is differentiated
in accordance with the interaction frequency data at block 2208.
The differentiated plotting of the aggregated correlation is
utilized at block 2210. For example, the differentiated plotting of
the aggregated correlation may be provided for presentation.
[0142] In an example embodiment, the differentiated plotting may be
used to determine users' tendency to interact with others with
respect of their reputation information (e.g., feedback scores).
For example, the differentiated plotting may help identify whether
users with high reputation information (e.g., high feedback scores)
tend to interact with other users that also have high reputation
information. Assortative mixing may be used to show the extent to
which nodes (e.g., users) connect preferentially to other nodes
with similar characteristics.
[0143] FIG. 23 illustrates a method 2300 for differentiated
plotting utilization according to an example embodiment. The method
2300 may be performed at block 2210 or otherwise performed.
[0144] Reputation information associated with assorted initiating
users and a assorted responding users in a social network for an
additional time period is accessed at block 2302.
[0145] At block 2304, interaction frequency data associated with
the assorted initiating users and the assorted responding users for
the additional time period is accessed.
[0146] The plotting of the aggregated correlation of the additional
time period is differentiated in accordance with the interaction
frequency data at block 2206.
[0147] The aggregated correlation between the assorted initiating
users and the assorted initiating users is plotted in accordance
with the reputation information at block 2308.
[0148] The differentiated plotting of the aggregated correlation
for the time period and the additional time period is used for the
analysis of the social network at block 2310.
[0149] FIGS. 24-26 are diagrams of example differentiated plottings
2400, 2500, 2600. The differentiated plottings 2400, 2500, 2600 are
example representations of differentiated plottings that may be
plotted in accordance with the method 2200 and/or the method 2300.
However, other representations of the differentiated plottings may
also be used.
[0150] The differentiated plottings 2400, 2500, 2600 plot the
aggregated correlation between initiating users' reputation
information (e.g., sellers' feedback scores) and receiving users'
reputation information (e.g., buyers' feedback scores).
[0151] The x axis of the differentiated plottings 2400, 2500, 2600
denotes users' reputation information, and the y axis denotes
receiving users reputation information. The axes of the
differentiated plottings 2400, 2500, 2600 may be logarithmically
binned.
[0152] The differentiation on the differentiated plottings 2400,
2500, 2600 based on interaction frequency data may be color. For
example, a score of zero to two hundred is reflected by a dark blue
color, a score of two hundred to four hundred is reflect by a
medium blue color, a score of four hundred to six hundred is
reflected by a light blue color, a score of six hundred to eight
hundred is reflected by a blue/green color, a score of eight
hundred to one thousand is reflected by a green/yellow color, a
score of one thousand to one thousand two hundred is reflect by a
yellow/orange color, a score of one thousand two hundred to one
thousand four hundred is reflect by an orange/red color, and one
thousand four hundred is reflected by a red color. The colors in
the legend are shown in the differentiated plottings 2400, 2500,
2600 as being gradient. However other types of representations of
the differentiation may be used.
[0153] The color of each block of the differentiated plottings
2400, 2500, 2600 may be determined by the number of interactions
(e.g., transactions) that happed between pairs of users with
corresponding reputation information.
[0154] The differentiated plotting 2400 may represent a number of
transactions of a particular category (e.g., crafts) in which most
interactions (e.g., transactions) are between initiating users
(e.g., buyers) that have a feedback score between twenty and five
hundred and responding users (e.g., sellers) that have a feedback
score between ten and ninety.
[0155] The differentiated plotting 2500 may represent a number of
transactions of a particular category in which most interactions
are between initiating users that have a feedback score between
sixteen and sixty and responding users that have a feedback score
between one hundred fifty and two seven hundred.
[0156] The differentiated plotting 2600 may represent a number of
transactions of a particular category (e.g., collectables) in which
most interactions are between initiating users that have a feedback
score between ten and seventy and responding users that have a
feedback score between one hundred and three thousand.
[0157] Platform
[0158] FIG. 27 is a network diagram depicting a client-server
system 2700, within which one example embodiment may be deployed.
By way of example, a network 2704 may include the functionality of
the provider network 104, the network analyzer 106 may be deployed
within an application server 2718, and the client machines
102.1-102.n may include the functionality of a client machine 2710
or a client machine 2712. The system 100 may also be deployed in
other systems.
[0159] A networked system 2702, in the example forms of a
network-based marketplace or publication system, provides
server-side functionality, via a network 2704 (e.g., the Internet
or Wide Area Network (WAN)) to one or more clients. FIG. 27
illustrates, for example, a web client 2706 (e.g., a browser, such
as the Internet Explorer browser developed by Microsoft Corporation
of Redmond, Wash. State), and a programmatic client 2708 executing
on respective client machines 2710 and 2712.
[0160] An Application Program Interface (API) server 2714 and a web
server 2716 are coupled to, and provide programmatic and web
interfaces respectively to, one or more application servers 2718.
The application servers 2718 host one or more marketplace
applications 2720 and authentication providers 2722. The
application servers 2718 are, in turn, shown to be coupled to one
or more databases servers 2724 that facilitate access to one or
more databases 2726.
[0161] The marketplace applications 2720 may provide a number of
marketplace functions and services to users that access the
networked system 2702. The authentication providers 2722 may
likewise provide a number of payment services and functions to
users. The authentication providers 2722 may allow users to
accumulate value (e.g., in a commercial currency, such as the U.S.
dollar, or a proprietary currency, such as "points") in accounts,
and then later to redeem the accumulated value for products (e.g.,
goods or services) that are made available via the marketplace
applications 2720. While the marketplace and authentication
providers 2720 and 2722 are shown in FIG. 27 to both form part of
the networked system 2702, in alternative embodiments the
authentication providers 2722 may form part of a payment service
that is separate and distinct from the networked system 2702.
[0162] Further, while the system 2700 shown in FIG. 27 employs a
client-server architecture, the present invention is of course not
limited to such an architecture, and could equally well find
application in a distributed, or peer-to-peer, architecture system,
for example. The various marketplace and authentication providers
2720 and 2722 could also be implemented as standalone software
programs, which need not have networking capabilities.
[0163] The web client 2706 accesses the various marketplace and
authentication providers 2720 and 2722 via the web interface
supported by the web server 2716. Similarly, the programmatic
client 2708 accesses the various services and functions provided by
the marketplace and authentication providers 2720 and 2722 via the
programmatic interface provided by the API server 2714. The
programmatic client 2708 may, for example, be a seller application
(e.g., the TurboLister.TM. application developed by eBay Inc., of
San Jose, Calif.) to enable sellers to author and manage listings
on the networked system 2702 in an off-line manner, and to perform
batch-mode communications between the programmatic client 2708 and
the networked system 2702.
[0164] FIG. 27 also illustrates a third party application 2728,
executing on a third party server machine 2730, as having
programmatic access to the networked system 2702 via the
programmatic interface provided by the API server 2714. For
example, the third party application 2728 may, utilizing
information retrieved from the networked system 2702, support one
or more features or functions on a website hosted by the third
party. The third party may, for example, provide one or more
promotional, marketplace or payment functions that are supported by
the relevant applications of the networked system 2702.
[0165] FIG. 28 is a block diagram illustrating multiple
applications 2720 and 2722 that, in one example embodiment, are
provided as part of the networked system 2702 (see FIG. 27). The
applications 2720 may be hosted on dedicated or shared server
machines (not shown) that are communicatively coupled to enable
communications between server machines. The applications themselves
are communicatively coupled (e.g., via appropriate interfaces) to
each other and to various data sources, so as to allow information
to be passed between the applications or so as to allow the
applications to share and access common data. The applications may
furthermore access one or more databases 2726 via the database
servers 2724.
[0166] The networked system 2702 may provide a number of
publishing, listing and price-setting mechanisms whereby a seller
may list (or publish information concerning) goods or services for
sale, a buyer can express interest in or indicate a desire to
purchase such goods or services, and a price can be set for a
transaction pertaining to the goods or services. To this end, the
marketplace applications 2720 are shown to include at least one
publication application 1110 and one or more auction applications
2802 which support auction-format listing and price setting
mechanisms (e.g., English, Dutch, Vickrey, Chinese, Double, Reverse
auctions etc.). The various auction applications 2802 may also
provide a number of features in support of such auction-format
listings, such as a reserve price feature whereby a seller may
specify a reserve price in connection with a listing and a
proxy-bidding feature whereby a bidder may invoke automated proxy
bidding.
[0167] A number of fixed-price applications 2804 support
fixed-price listing formats (e.g., the traditional classified
advertisement-type listing or a catalogue listing) and buyout-type
listings. Specifically, buyout-type listings (e.g., including the
Buy-It-Now (BIN) technology developed by eBay Inc., of San Jose,
Calif.) may be offered in conjunction with auction-format listings,
and allow a buyer to purchase goods or services, which are also
being offered for sale via an auction, for a fixed-price that is
typically higher than the starting price of the auction.
[0168] Store applications 2806 allow a seller to group listings
within a "virtual" store, which may be branded and otherwise
personalized by and for the seller. Such a virtual store may also
offer promotions, incentives and features that are specific and
personalized to a relevant seller.
[0169] Reputation applications 2808 allow users that transact,
utilizing the networked system 2702, to establish, build and
maintain reputations, which may be made available and published to
potential trading partners. Consider that where, for example, the
networked system 2702 supports person-to-person trading, users may
otherwise have no history or other reference information whereby
the trustworthiness and credibility of potential trading partners
may be assessed. The reputation applications 2808 allow a user, for
example through feedback provided by other transaction partners, to
establish a reputation within the networked system 2702 over time.
Other potential trading partners may then reference such a
reputation for the purposes of assessing credibility and
trustworthiness.
[0170] Personalization applications 2810 allow users of the
networked system 2702 to personalize various aspects of their
interactions with the networked system 2702. For example a user
may, utilizing an appropriate personalization application 2810,
create a personalized reference page at which information regarding
transactions to which the user is (or has been) a party may be
viewed. Further, a personalization application 2810 may enable a
user to personalize listings and other aspects of their
interactions with the networked system 2702 and other parties.
[0171] The networked system 2702 may support a number of
marketplaces that are customized, for example, for specific
geographic regions. A version of the networked system 2702 may be
customized for the United Kingdom, whereas another version of the
networked system 2702 may be customized for the United States. Each
of these versions may operate as an independent marketplace, or may
be customized (or internationalized and/or localized) presentations
of a common underlying marketplace. The networked system 2702 may
accordingly include a number of internationalization applications
2812 that customize information (and/or the presentation of
information) by the networked system 2702 according to
predetermined criteria (e.g., geographic, demographic or
marketplace criteria). For example, the internationalization
applications 2812 may be used to support the customization of
information for a number of regional websites that are operated by
the networked system 2702 and that are accessible via respective
web servers 2716.
[0172] Navigation of the networked system 2702 may be facilitated
by one or more navigation applications 2814. For example, a search
application (as an example of a navigation application) may enable
key word searches of listings published via the networked system
2702. A browse application may allow users to browse various
category, catalogue, or system inventory structures according to
which listings may be classified within the networked system 2702.
Various other navigation applications may be provided to supplement
the search and browsing applications.
[0173] In order to make listings available via the networked system
2702 as visually informing and attractive as possible, the
marketplace applications 2720 may include one or more imaging
applications 2816 utilizing which users may upload images for
inclusion within listings. An imaging application 2816 also
operates to incorporate images within viewed listings. The imaging
applications 2816 may also support one or more promotional
features, such as image galleries that are presented to potential
buyers. For example, sellers may pay an additional fee to have an
image included within a gallery of images for promoted items.
[0174] Listing creation applications 2818 allow sellers
conveniently to author listings pertaining to goods or services
that they wish to transact via the networked system 2702, and
listing management applications 2820 allow sellers to manage such
listings. Specifically, where a particular seller has authored
and/or published a large number of listings, the management of such
listings may present a challenge. The listing management
applications 2820 provide a number of features (e.g.,
auto-relisting, inventory level monitors, etc.) to assist the
seller in managing such listings. One or more post-listing
management applications 2822 also assist sellers with a number of
activities that typically occur post-listing. For example, upon
completion of an auction facilitated by one or more auction
applications 2802, a seller may wish to leave feedback regarding a
particular buyer. To this end, a post-listing management
application 2822 may provide an interface to one or more reputation
applications 2808, so as to allow the seller conveniently to
provide feedback regarding multiple buyers to the reputation
applications 2808.
[0175] Dispute resolution applications 2824 provide mechanisms
whereby disputes arising between transacting parties may be
resolved. For example, the dispute resolution applications 2824 may
provide guided procedures whereby the parties are guided through a
number of steps in an attempt to settle a dispute. In the event
that the dispute cannot be settled via the guided procedures, the
dispute may be escalated to a merchant mediator or arbitrator.
[0176] A number of fraud prevention applications 2826 implement
fraud detection and prevention mechanisms to reduce the occurrence
of fraud within the networked system 2702.
[0177] Messaging applications 2828 are responsible for the
generation and delivery of messages to users of the networked
system 2702, such messages for example advising users regarding the
status of listings at the networked system 2702 (e.g., providing
"outbid" notices to bidders during an auction process or to provide
promotional and merchandising information to users). Respective
messaging applications 2828 may utilize any one have a number of
message delivery networks and platforms to deliver messages to
users. For example, messaging applications 2828 may deliver
electronic mail (e-mail), instant message (IM), Short Message
Service (SMS), text, facsimile, or voice (e.g., Voice over IP
(VoIP)) messages via the wired (e.g., the Internet), Plain Old
Telephone Service (POTS), or wireless (e.g., mobile, cellular,
WiFi, WiMAX) networks.
[0178] Merchandising applications 2830 support various
merchandising functions that are made available to sellers to
enable sellers to increase sales via the networked system 2702. The
merchandising applications 2830 also operate the various
merchandising features that may be invoked by sellers, and may
monitor and track the success of merchandising strategies employed
by sellers.
[0179] The networked system 2702 itself, or one or more parties
that transact via the networked system 2702, may operate loyalty
programs that are supported by one or more loyalty/promotions
applications 2832. For example, a buyer may earn loyalty or
promotions points for each transaction established and/or concluded
with a particular seller, and may be offered a reward for which
accumulated loyalty points can be redeemed.
[0180] A network analyzer application 2834 may analyze the social
network amount a number of users of the system 100.
[0181] FIG. 29 shows a diagrammatic representation of machine in
the example form of a computer system 2900 within which a set of
instructions may be executed causing the machine to perform any one
or more of the methods, processes, operations, or methodologies
discussed herein. The network analyzer 106 may operate on or more
computer systems 2900 and/or the client machines 102.1-102.n may
include the functionality of the computer system 2900.
[0182] In an example embodiment, the machine operates as a
standalone device or may be connected (e.g., networked) to other
machines. In a networked deployment, the machine may operate in the
capacity of a server or a client machine in server-client network
environment, or as a peer machine in a peer-to-peer (or
distributed) network environment. The machine may be a server
computer, a client computer, a personal computer (PC), a tablet PC,
a set-top box (STB), a Personal Digital Assistant (PDA), a cellular
telephone, a web appliance, a network router, switch or bridge, or
any machine capable of executing a set of instructions (sequential
or otherwise) that specify actions to be taken by that machine.
Further, while only a single machine is illustrated, the term
"machine" shall also be taken to include any collection of machines
that individually or jointly execute a set (or multiple sets) of
instructions to perform any one or more of the methodologies
discussed herein.
[0183] The example computer system 2900 includes a processor 2902
(e.g., a central processing unit (CPU) a graphics processing unit
(GPU) or both), a main memory 2904 and a static memory 2906, which
communicate with each other via a bus 2908. The computer system
2900 may further include a video display unit 2910 (e.g., a liquid
crystal display (LCD) or a cathode ray tube (CRT)). The computer
system 2900 also includes an alphanumeric input device 2912 (e.g.,
a keyboard), a cursor control device 2914 (e.g., a mouse), a drive
unit 2916, a signal generation device 2918 (e.g., a speaker) and a
network interface device 2920.
[0184] The drive unit 2916 includes a machine-readable medium 2922
on which is stored one or more sets of instructions (e.g., software
2924) embodying any one or more of the methodologies or functions
described herein. The software 2924 may also reside, completely or
at least partially, within the main memory 2904 and/or within the
processor 2902 during execution thereof by the computer system
2900, the main memory 2904 and the processor 2902 also constituting
machine-readable media.
[0185] The software 2924 may further be transmitted or received
over a network 2926 via the network interface device 2920.
[0186] While the machine-readable medium 2922 is shown in an
example embodiment to be a single medium, the term
"machine-readable medium" should be taken to include a single
medium or multiple media (e.g., a centralized or distributed
database, and/or associated caches and servers) that store the one
or more sets of instructions. The term "machine-readable medium"
shall also be taken to include any medium that is capable of
storing, encoding or carrying a set of instructions for execution
by the machine and that cause the machine to perform any one or
more of the methodologies of the present invention. The term
"machine-readable medium" shall accordingly be taken to include,
but not be limited to, solid-state memories, optical and magnetic
media, and carrier wave signals.
[0187] Certain systems, apparatus, applications or processes are
described herein as including a number of modules or mechanisms. A
module or a mechanism may be a unit of distinct functionality that
can provide information to, and receive information from, other
modules. Accordingly, the described modules may be regarded as
being communicatively coupled. Modules may also initiate
communication with input or output devices, and can operate on a
resource (e.g., a collection of information). The modules be
implemented as hardware circuitry, optical components, single or
multi-processor circuits, memory circuits, software program modules
and objects, firmware, and combinations thereof, as appropriate for
particular implementations of various embodiments.
[0188] Thus, methods and systems for social network analysis have
been described. Although the present invention has been described
with reference to specific example embodiments, it will be evident
that various modifications and changes may be made to these
embodiments without departing from the broader spirit and scope of
the invention. Accordingly, the specification and drawings are to
be regarded in an illustrative rather than a restrictive sense.
[0189] The Abstract of the Disclosure is provided to comply with 37
C.F.R. .sctn.1.72(b), requiring an abstract that will allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. In addition,
in the foregoing Detailed Description, it can be seen that various
features are grouped together in a single embodiment for the
purpose of streamlining the disclosure. This method of disclosure
is not to be interpreted as reflecting an intention that the
claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter lies in less than all features of a single
disclosed embodiment. Thus the following claims are hereby
incorporated into the Detailed Description, with each claim
standing on its own as a separate embodiment.
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