Method and system for social network analysis

Abstract

Methods and system for social commerce network analysis are described. In one embodiment, user interaction data associated with users for a time period in a social commerce network is accessed and a network analysis is performed. Users within the social commerce network may be selected where each of the users is associated with reputation information. A motif for the users for the time period based on the network analysis may be generated. A node of the motif is associated with a particular user, and the motif defines an expected relationship between the users in the social commerce network. The node is distinguished based on the reputation information of the user. The motif may be used with a plurality of distinguished nodes for analysis of the social commerce network.

1 . A method comprising:
 accessing user interaction data associated with a plurality of users for a time period in a social commerce network;   performing network analysis, using one or more processors, on the user interaction data;   selecting a plurality of example users within the social commerce network, each of the plurality of 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 plurality of example users, the motif defining an expected relationship between the plurality of example users in the social commerce 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 commerce network.   
     
     
         2 . The method of  claim 1 , wherein the utilizing comprises:
 providing the motif with the plurality of distinguished nodes for presentation.   
     
     
         3 . The method of  claim 1 , wherein the utilizing comprises:
 analyzing the motif and a plurality of additional motifs; and   making a decision regarding the social commerce network in accordance with the analyzing of the motif.   
     
     
         4 . The method of  claim 1 , 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.   
     
     
         5 . The method of  claim 1 , 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.   
     
     
         6 . The method of  claim 1 , wherein the distinguishing comprises:
 colorizing the node of the plurality of example users in accordance with the reputation information.   
     
     
         7 . The method of  claim 1 , wherein the social commerce network comprises a plurality of buyers and a plurality of sellers. 
     
     
         8 . The method of  claim 1 , further comprising identifying a category and wherein the accessing of the user interaction data is based on the identified category. 
     
     
         9 . The method of  claim 1 , wherein the motif is a triad. 
     
     
         10 . A non-transitory machine-readable medium comprising instructions, which when implemented by one or more processors perform operations comprising:
 accessing user interaction data associated with a plurality of users for a time period in a social commerce network;   performing network analysis, using the one or more processors, on the user interaction data;   selecting a plurality of example users within the social commerce network, each of the plurality of 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 plurality of example users, the motif defining an expected relationship between the plurality of example users in the social commerce 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 commerce network.   
     
     
         11 . The non-transitory machine-readable medium of  claim 10  further comprising additional instructions, which when implemented by the one or more processors perform operations 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. 
 
     
     
         12 . The non-transitory machine-readable medium of  claim 10 , wherein the utilizing comprises:
 providing the motif with the plurality of distinguished nodes for presentation.   
     
     
         13 . The non-transitory machine-readable medium of  claim 10 , wherein the utilizing comprises:
 analyzing the motif and a plurality of additional motifs; and   making a decision regarding the social commerce network in accordance with the analyzing of the motif.   
     
     
         14 . The non-transitory machine-readable medium of  claim 10  further comprising additional instructions, which when implemented by the one or more processors perform operations 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. 
 
     
     
         15 . The non-transitory machine-readable medium of  claim 10 , wherein the distinguishing comprises:
 colorizing the node of the plurality of example users in accordance with the reputation information.   
     
     
         16 . The non-transitory machine-readable medium of  claim 10 , wherein the social commerce network comprises a plurality of buyers and a plurality of sellers. 
     
     
         17 . The non-transitory machine-readable medium of  claim 10 , further comprising identifying a category and wherein the accessing of the user interaction data is based on the identified category. 
     
     
         18 . 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, using one or more processors, the plotting of the aggregated correlation in accordance with the interaction frequency data; and   utilizing the differentiated plotting of the aggregated correlation.   
     
     
         19 . The method of  claim 18 , wherein the utilizing comprises:
 providing the differentiated plotting of the aggregated correlation for presentation.   
     
     
         20 . The method of  claim 18 , wherein the utilizing comprises:
 accessing the reputation information associated with a plurality of assorted initiating users and a plurality of assorted responding users in the 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 analysis of the social network.