METHOD AND SYSTEM FOR SOCIAL NETWORK ANALYSIS

• US 20090070460A1
• Filed: 12/30/2007
• Published: 03/12/2009
• Est. Priority Date: 09/12/2007
• Status: Abandoned Application

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Shen, Zeqian, Sundaresan, Neelakantan
• Application Number:
  US11/967,222
• Publication Number:
  US 20090070460A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Methods and system for social network analysis are described. In one 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.

Claims

1. A method comprising:

accessing 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;

calculating a social strength of the social network for the time period by taking 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; and

utilizing the social strength of the social network for the time period for analysis of the social network,

wherein the strongly connected component value has greatest weight and the disconnected component value has lowest weight in the combining.

2. The method of claim 1, wherein the accessing comprises:

determining the strongly connected component value in accordance with a graph percentage of a strongly connected component of the social network;

determining the in-component value in accordance with the graph percentage of an in-component of the social network;

determining the out-component value in accordance with the graph percentage of an out-component of the social network;

determining the disconnected component value in accordance with the graph percentage of a disconnected component of the social network;

determining the tendril value in accordance with the graph percentage of one or more tendrils of the social network; and

determining the tube value in accordance with the graph percentage of a tube of the social network.

3. The method of claim 1, wherein the utilizing comprises:

providing the social strength of the social network for the time period for presentation.

4. The method of claim 1, wherein the utilizing comprises:

accessing 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;

calculating the social strength of the social network for the additional 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; and

using the social strength of the social network for the time period and the additional time period for analysis of the social network.

5. The method of claim 4, wherein the using comprises:

providing a difference between the social strength of the social network for the time period and the additional time period for presentation.

6. The method of claim 1, wherein the utilizing comprises:

providing the social strength for a plurality of categories in the social network for the time period for presentation;

wherein the social strength is calculated for the plurality of categories in the social network for the time period.

7. The method of claim 6, further comprising:

accessing 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;

calculating the social strength of the social network for the additional 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; and

using the social strength for a plurality of categories of the social network for the time period and the additional time period for analysis of the social network,

wherein the social strength is calculated for the plurality of categories in the social network for the time period and the additional time period.

8. The method of claim 1, wherein a weight of the strongly connected component value is double the weight of the in-component and the out-component and the weight of the disconnected component value is half the weight of the in-component and the out-component in the combining.

9. The method of claim 1, wherein the social network is a social network.

10. A method comprising:

accessing 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;

calculating a social strength of the social network 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 the tube value;

identifying one or more users associated with the strongly connected component, the strongly connected component value being a value of the strongly connected component for the time period;

modifying an aspect of the social network associated with the one or more users;

accessing 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, the additional time period being after the modifying of the aspect;

calculating the social strength of the social network for the additional 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; and

utilizing the social strength of the social network for the time period and the additional time period for analysis in accordance with the modifying of the aspect of the social network.

11. The method of claim 10, wherein the modifying of the aspect comprises:

providing the one or more users with an incentive to have a plurality of other users utilize a feature of the social network.

12. The method of claim 10, wherein the modifying of the aspect comprises:

providing the one or more users with a designated status in the social network.

13. A method comprising:

accessing reputation information associated with a plurality of initiating users and a plurality of responding users in a social network for a time period;

accessing interaction frequency data associated with the plurality of initiating users and the plurality of responding users for the time period;

plotting an aggregated correlation between the plurality of initiating users and the plurality of responding users in accordance with the reputation information;

differentiating the plotting of the aggregated correlation in accordance with the interaction frequency data; and

utilizing the differentiated plotting of the aggregated correlation.

14. The method of claim 13, wherein the utilizing comprises:

providing the differentiated plotting of the aggregated correlation for presentation.

15. The method of claim 13, wherein the utilizing comprises:

accessing the reputation information associated with a plurality of assorted initiating users and a plurality of assorted responding users in a social network for an additional time period;

accessing interaction frequency data associated with the plurality of assorted initiating users and the plurality of assorted responding users for the additional time period;

plotting the aggregated correlation between the plurality of assorted initiating users and the plurality of assorted initiating users in accordance with the reputation information; and

using the differentiated plotting of the aggregated correlation for the time period and the additional time period for the analysis of the social network.

16. A machine-readable medium comprising instructions, which when implemented by one or more processors perform the following operations:

access 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;

calculate a social strength of the social network for the time period by taking 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; and

utilize the social strength of the social network for the time period for analysis of the social network,

wherein the strongly connected component value has greatest weight and the disconnected component value has lowest weight in the combining.

17. The machine-readable medium of claim 16, wherein the one or more operations to access include:

determine the strongly connected component value in accordance with a graph percentage of a strongly connected component of the social network;

determine the in-component value in accordance with the graph percentage of an in-component of the social network;

determine the out-component value in accordance with the graph percentage of an out-component of the social network;

determine the disconnected component value in accordance with the graph percentage of a disconnected component of the social network;

determine the tendril value in accordance with the graph percentage of one or more tendrils of the social network; and

determine the tube value in accordance with the graph percentage of a tube of the social network.

18. The machine-readable medium of claim 16, wherein the one or more operations to utilize include:

provide the social strength for a plurality of categories in the social network for the time period for presentation;

wherein the social strength is calculated for the plurality of categories in the social network for the time period.

19. A system comprising:

a value access module to access 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;

a social strength calculation module to calculate a social strength of the social network 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 the tube value accessed by the value access module; and

a social strength provider module to provide the social strength of the social network for the time period calculated by the social strength calculated module for presentation,

wherein the strongly connected component value has greatest weight and the disconnected component value has lowest weight in the combining.

20. The system of claim 19, further comprising:

a difference provider module to provide a difference between the social strength of the social network for the time period and an additional time period for presentation, wherein

the value access module further accesses 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 the additional time period; and

the social strength calculation module further calculates the social strength of the social network for the additional 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.

21. The system of claim 19, wherein the social network is a social network.