Method and system for social network analysis

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.

1 . (canceled) 
     
     
         2 . A system comprising:
 a non-transitory memory to store user interaction data describing user interactions in a social network;   one or more processors configured to perform operations comprising:
 identifying a portion of the user interaction data according to a transaction category assigned to the user interaction data; 
 generating a graph of the portion of the user interaction data wherein:
 a strongly connected component value quantifies a number of strongly connected vertices where, for each strongly connected vertex, there is a first path from a first vertex to a second vertex and a second path from the second vertex to the first vertex, the first vertex and the second vertex corresponding to a first user and a second user, respectively, who interact between themselves in the social network, 
 an in-component value quantifies a number of in vertices, each in vertex corresponding to a third user who interacts with the first user, 
 an out-component value quantifies a number of out vertices, each out vertex corresponding to a fourth user who interacts with the second user, 
 a tube value quantifies a number of tube vertices, each tube vertex being between the third user and the fourth user that do not include the first user and the second user, 
 a tendril value quantifies a number of tendril vertices, each tendril vertex being a further user who has interacted with the third user or the fourth user but has not interacted with the first user or the second user, 
 a disconnected component value quantifying a number of vertices that are not quantified by the strongly connected component value, the in-component value, the out-component value, the tendril value, or the tube value; and 
 
 measuring a social strength of the social network. 
   
     
     
         3 . The system of  claim 2 , wherein the portion of the user interaction data is further identified based on a time period during which the portion of the user interaction data was captured. 
     
     
         4 . The system of  claim 3 , wherein the operations further comprise identifying a second portion of the user interaction data according to the transaction category and a second time period during which the second portion of the user interaction data was captured. 
     
     
         5 . The system of  claim 4 , further comprising providing a difference between the social strength of the social network for the time period and the second time period. 
     
     
         6 . The system of  claim 2 , wherein the operations further comprise accessing reputation information of each of a plurality of users of the social network and applying a texture to the graph based at least in part on the reputation information. 
     
     
         7 . The system of  claim 2 , wherein the operations further comprise accessing interaction frequency data of each of a plurality of users of the social network and applying a texture to the graph based at least in part on the interaction frequency data. 
     
     
         8 . The system of  claim 2 , wherein the operations further comprise accessing transactional financial data of each of the plurality of users of the social network and applying a texture to the graph based at least in part in the transactional financial data. 
     
     
         9 . The system of  claim 2 , wherein the operations further comprise providing a display of the graph. 
     
     
         10 . The system of  claim 2 , wherein the operations further comprise identifying a second portion of the user interaction data according to a second transaction category assigned to the user interaction data and generating a second graph of the second portion of the user interaction data. 
     
     
         11 . The system of  claim 10 , wherein the operations further comprise providing a display of the graph of the portion of the user interaction data and the second graph of the second portion of the user interaction data. 
     
     
         12 . The system of  claim 10 , wherein the operations further comprise determining differences between the graph of the portion of the user interaction data and the second graph of the second portion of the user interaction data. 
     
     
         13 . The system of  claim 12 , wherein the operations further comprise generating a bar graph indicating the differences including comparative social strengths of the graph of the portion of the user interaction data and the second graph of the second portion of the user interaction data. 
     
     
         14 . The system of  claim 13 , wherein the bar graph indicates a relative magnitude of the strongly connected component value, the in-component value, the out-component value, the tube value, the tendril value, and the disconnected component value. 
     
     
         15 . The system of  claim 13 , wherein the operations further comprise providing a measurement indicating an asymmetry of the graph of the portion of the user interaction data and the second graph of the second portion of the user interaction data. 
     
     
         16 . A method comprising:
 identifying a portion of the user interaction data according to a transaction category assigned to the user interaction data;   using one or more processors, generating a graph of the portion of the user interaction data wherein:
 a strongly connected component value quantifies a number of strongly connected vertices where, for each strongly connected vertex, there is a first path from a first vertex to a second vertex and a second path from the second vertex to the first vertex, the first vertex and the second vertex corresponding to a first user and a second user, respectively, who interact between themselves in the social network, 
 an in-component value quantifies a number of in vertices, each in vertex corresponding to a third user who interacts with the first user, 
 an out-component value quantifies a number of out vertices, each out vertex corresponding to a fourth user who interacts with the second user, 
 a tube value quantifies a number of tube vertices, each tube vertex being between the third user and the fourth user that do not include the first user and the second user, 
 a tendril value quantifies a number of tendril vertices, each tendril vertex being a further user who has interacted with the third user or the fourth user but has not interacted with the first user or the second user, 
 a disconnected component value quantifying a number of vertices that are not quantified by the strongly connected component value, the in-component value, the out-component value, the tendril value, or the tube value; and 
   measuring a social strength of the social network.   
     
     
         17 . The method of  claim 16 , wherein the portion of the user interaction data is further identified based on a time period during which the portion of the user interaction data was captured. 
     
     
         18 . The method of  claim 17 , further comprising identifying a second portion of the user interaction data according to the transaction category and a second time period during which the second portion of the user interaction data was captured. 
     
     
         19 . The method of  claim 18 , further comprising providing a difference between the social strength of the social network for the time period and the second time period. 
     
     
         20 . The method of  claim 16 , further comprising identifying a second portion of the user interaction data according to a second transaction category assigned to the user interaction data and generating a second graph of the second portion of the user interaction data. 
     
     
         21 . A non-transitory computer-readable medium having instructions embodied thereon, the instructions executable by one or more processors for performing operations comprising:
 identifying a portion of the user interaction data according to a transaction category assigned to the user interaction data;   generating a graph of the portion of the user interaction data wherein:
 a strongly connected component value quantifies a number of strongly connected vertices where, for each strongly connected vertex, there is a first path from a first vertex to a second vertex and a second path from the second vertex to the first vertex, the first vertex and the second vertex corresponding to a first user and a second user, respectively, who interact between themselves in the social network, 
 an in-component value quantifies a number of in vertices, each in vertex corresponding to a third user who interacts with the first user, 
 an out-component value quantifies a number of out vertices, each out vertex corresponding to a fourth user who interacts with the second user, 
 a tube value quantifies a number of tube vertices, each tube vertex being between the third user and the fourth user that do not include the first user and the second user, 
 ma tendril value quantifies a number of tendril vertices, each tendril vertex being a further user who has interacted with the third user or the fourth user but has not interacted with the first user or the second user, 
 a disconnected component value quantifying a number of vertices that are not quantified by the strongly connected component value, the in-component value, the out-component value, the tendril value, or the tube value; and 
   measuring a social strength of the social network.