Identifying communities within a social network based on information propagation data

1. A method comprising:

receiving a social graph, the social graph including nodes and relationships between the nodes;

receiving a number of communities to find within the social graph;

receiving data regarding propagation of information between the nodes;

calculating a probability of formation of a link between a first one of the nodes and a second one of the nodes based on the data, the link providing a direction of flow of media between the first and second nodes, wherein the probability of formation of the link includes a probability that the media will be sent from the first node to the second node, wherein the probability that the media will be sent from the first node to the second node is based on a level of publication of at least some of the information within one of the communities by the first node; and

calculating a probability that the media will be accessed by the second node based on the data,

wherein the method is executed by a processor.

2. The method of claim 1 , wherein the data regarding propagation of information includes one or more identifiers of the information and one or more times at which the information is propagated from the first node to one or more of the nodes.

3. The method of claim 1 , wherein the probability of formation of the link includes a sum over all the communities of a product of a first probability, a second probability, and a level of involvement of the first and second nodes within the community, wherein the first probability includes a probability of active involvement of the first node within the community, wherein the second probability includes a probability of passive involvement of the second node within the community, and wherein the probability of active involvement of the first node is based on the level of publication by the first node.

4. The method of claim 1 , wherein the probability that the media will be accessed by the second node includes a first sum over all the communities of a second sum, the second sum being over all active nodes of the social graph of a product of a first probability, a second probability, and a level of involvement of the first and second nodes within the community, wherein the first probability is that the first node will be an influencer of the second node within the community, and the second probability is that the second node will be an influencee of the first node within the community.

5. A method comprising:

receiving a social graph, the social graph including nodes;

receiving a number of communities to find within the social graph;

receiving data regarding propagation of information between two or more of the nodes of the social graph;

for each community, determining a probability of formation of a link between a first one of the nodes and a second one of the nodes, the link providing a direction of propagation of additional information from the first node to the second node, wherein the probability of formation of the link includes a probability that the additional information will be sent from the first node to the second node, wherein the probability that the additional information will be sent from the first node to the second node is based on a level of publication of at least some of the information within the community by the first node; and

for each community, determining a probability of occurrence of an activation in the community of the second node, the activation including a passive involvement of accessing the additional information,

wherein the method is executed by a processor.

6. The method of claim 5 , wherein each node is associated with a social network service account.

7. The method of claim 5 , wherein each community is identified using a title of media, or a name of the media, or a topic of the media, or a subject matter of the media, or content of the media, or a keyword extracted from the media, or metadata about the media, or a combination thereof.

8. The method of claim 5 , wherein the data received regarding propagation of the information includes a title of media, or a name of the media, or a topic of the media, or a subject matter of the media, or content of the media, or a keyword extracted from the media, or metadata about the media, or a combination thereof.

9. The method of claim 5 , further comprising:

determining a level of active involvement for each node and for each community of the social graph, wherein the level of active involvement includes the level of publication; and

determining a level of passive involvement for each node and for each community of the social graph.

10. The method of claim 9 , wherein the active involvement includes generating the information and posting the information or posting the information to a social network server.

11. The method of claim 5 , wherein the passive involvement excludes generating the information and posting the information to a social network server.

12. The method of claim 5 , further comprising:

for each community, determining a level of active involvement of the first node within the community, wherein the level of active involvement includes the level of publication;

for each community, determining a level of passive involvement of the second node;

for each community, determining a probability of active involvement of the first node based on the level of active involvement of the first node; and

for each community, determining a probability of passive involvement of the second node based on the level of passive involvement of the second node.

13. The method of claim 12 , wherein determining the probability of formation of the link between the first and second nodes is based on the probability of active involvement and the probability of passive involvement.

14. The method of claim 5 , further comprising:

for each community, determining a level of active involvement of the first node, wherein the level of active involvement includes the level of publication;

for each community, determining a level of passive involvement of the second node;

for each community, determining a probability that the first node will be an influencer of the activation based on the level of active involvement of the first node; and

for each community, determining a probability that the second node of the social graph will be influenced by the activation based on the level of passive involvement of the second node.

15. The method of claim 14 , wherein determining the probability of occurrence of the activation within the social graph is based on a probability that the first node will be the influencer of the activation and the probability that the second node will be influenced by the activation.

16. The method of claim 5 , wherein the activation includes a reception of the additional information by the second node from the first node and access of media by the second node based on the additional information.

17. The method of claim 5 , wherein determining of the probability of formation of the link between the first and second nodes is performed simultaneous with determining the probability of occurrence of the activation in the community of the second node.

18. A method for identifying communities based on information propagation data, the method comprising:

receiving a social graph, the social graph including nodes and relationships between the nodes;

receiving a number of the communities to find within the social graph;

receiving data regarding propagation of information between two or more of the nodes of the social graph;

for each community, determining a probability of existence of a link between a first one of the nodes and a second one of the nodes, the link providing a direction of propagation of additional information between the first and second nodes; and

for each community, determining a probability of occurrence of an activation in the community of one or more of the nodes, the activation including a passive involvement of accessing the additional information;

determining a level of active involvement of the first node within one of the communities of the social graph;

determining a level of the passive involvement of the second node within the community;

determining a probability that the first node will be an influencer of the activation within the community based on the level of active involvement of the first node within the community;

determining a probability that the second node of the social graph will be influenced by the activation within the community based on the level of passive involvement of the second node within the community,

wherein determining the probability of occurrence of the activation within the social graph is based on a probability that the first node will be the influencer of the activation and the probability that the second node will be influenced by the activation; and

determining a probability of a fit between model parameters and the data regarding propagation of the information and the social graph based on the probability of occurrence of the activation within the social graph and the probability of the existence of the link between the first node and the second node,

wherein the method is executed by a processor.

19. The method of claim 18 , wherein the probability of the existence of the link includes a probability that the additional information will be sent from the first node to the second node.

20. The method of claim 18 , wherein the level of active involvement includes a level of publication of the information by the first node to one or more of remaining ones of the nodes, wherein the level of the publication includes a level of posting the information within a social network identified by the social graph.