METHOD AND SYSTEM FOR SOCIAL NETWORK ANALYSIS

• US 20110071953A1
• Filed: 11/30/2010
• Published: 03/24/2011
• Est. Priority Date: 09/12/2007
• Status: Active Grant

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Shen, Zeqian, Sundaresan, Neelakantan
• Granted Patent: US 8,473,422 B2
• Time in Patent Office: Days
• US Class Current: 705/319
• 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 commerce 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 commerce network for a time period;

calculating a social strength of the social commerce 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 commerce network for the time period for analysis of the social commerce 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 commerce network;

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

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

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

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

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

3. 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 commerce network for an additional time period;

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

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

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

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

5. The method of claim 4, 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 commerce network for an additional time period;

calculating the social strength of the social commerce 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 the plurality of categories of the social commerce network for the time period and the additional time period for analysis of the social commerce network,

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

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

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

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 commerce network for a time period;

calculating a social strength of the social commerce 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, the strongly connected component value having greatest weight and the disconnected component value having lowest weight in the combining; and

utilizing the social strength of the social commerce network for the time period for analysis of the social commerce network.

8. The non-transitory machine-readable medium of claim 7, wherein the operation to access includes:

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

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

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

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

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

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

9. The non-transitory machine-readable medium of claim 7, wherein the operations to utilize include:

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

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

10. A system comprising:

a non-transitory memory to store one or more user interactions in a social commerce network; and

one or more processors configured to perform operations 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 the social commerce network for a time period based on the one or more user interactions;

calculating a social strength of the social commerce 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, the strongly connected component value having greatest weight and the disconnected component value having lowest weight in the combining; and

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

11. The system of claim 10, wherein the one or more processors are further configured to perform operations comprising:

providing a difference between the social strength of the social commerce network for the time period and an additional time period for presentation,

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 commerce network for the additional time period; and

calculating the social strength of the social commerce 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.

12. A method comprising:

accessing user interaction data of a time period for a plurality of users in a social commerce 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 commerce network.

13. The method of claim 12, further comprising:

accessing reputation information associated with each of the plurality of users; and

applying a texture to the necktie-shaped graph in accordance with the reputation information.

14. The method of claim 12, 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.

15. The method of claim 12, further comprising:

accessing transactional financial data associated with each of the plurality of users; and

applying a texture to the necktie-shaped graph in accordance with the transactional financial data.

16. The method of claim 12, wherein the utilizing comprises:

providing the necktie-shaped graph for presentation.

17. The method of claim 12, wherein the utilizing comprises:

accessing additional user interaction data associated with the social commerce 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 commerce network.

18. The method of claim 17, wherein the utilizing comprises:

providing a difference between the necktie-shaped graph and the additional necktie-shaped graph for presentation.

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

accessing user interaction data of a time period for a plurality of users in a social commerce 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 commerce network.

20. The non-transitory machine-readable medium of claim 19, wherein the one or more operations to generate the necktie-shaped graph include:

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

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.