METHOD AND SYSTEM FOR MAXIMIZING CONTENT SPREAD IN SOCIAL NETWORK

• US 20120259915A1
• Filed: 04/06/2011
• Published: 10/11/2012
• Est. Priority Date: 04/06/2011
• Status: Active Grant

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Rastogi, Rajeev, Ranu, Sayan, Bhatt, Rushi Prafull, Chaoji, Vineet Shashikant
• Granted Patent: US 8,751,618 B2
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A method, a system and a computer program product for maximizing content spread in a social network are provided. Samples of edges are generated from an initial candidate set of edges. Each edge of the samples of edges has a probability value for content flow. Further, a subset of edges is determined from the samples of edges based on gain corresponding to each edge. Also, each node of the subset of edges is having at least one of less than ‘K’ or equal to ‘K’ incoming edges. Further, the probability of each edge, of the subset of edges, may be incremented. Furthermore, a final set of edges may be determined by ensuring ‘K’ incoming edges. The ‘K’ incoming edges may be ensured by removing one or more incoming edges when a number of the incoming edges for a node of the final set is greater than ‘K’ incoming edge.

Claims

1. A method for maximizing content spread in a social network, the social network comprising a set of nodes and a set of edges between one or more nodes of the set of nodes, the method comprising:

executing steps (a) to (d) for performing one or more functionalities to determine a subset of edges relevant for maximizing flow of a content in the social network, the steps (a) to (d) comprising;

(a) generating one or more samples of edges from an initial candidate set of edges, each edge acquiring a probability value for content flow thereto;

(b) computing gain corresponding to each edge of the one or more samples of edges;

(c) determining the subset of edges from the one or more samples of edges, the subset of edges being determined based on the gain, each node corresponding to each edge of the subset of edges having at least one of less than ‘

K’

incoming edges and equal to ‘

K’

incoming edges; and

(d) incrementing the probability value of each edge of the subset of edges by a predefined value, the probability value of each edge of the subset of edges being incremented to upgrade the determined subset of edges,

wherein the steps (a) to (d) being performed for a predefined number of iterations;

determining a final set of edges ‘

X’

from the upgraded subset of edges, the final set of edges ‘

X’

being determined by ensuring ‘

K’

incoming edges for each node of the upgraded set of edges; and

outputting the final set of edges ‘

X’

to maximize spreading of the content in the social network.

2. The method of claim 1, wherein each node of the set of nodes corresponding to a user of the social network.

3. The method of claim 1, wherein each edge of the set of edges corresponding to a connection between two users of the social network.

4. The method of claim 1 further comprising identifying the initial candidate set of edges comprising candidate edges for a node, the initial candidate set being identified based on one or more characteristics corresponding to the node.

5. The method of claim 1, wherein the probability value for each edge is initialized as ‘

0’

.

6. The method of claim 1, wherein determining the subset of edges from the one or more samples of edges comprising determining one or more edges having maximum total gain.

7. The method of claim 1, wherein ‘

K’

is a predefined number of incoming edges for the each node in the subset of edges.

8. The method of claim 1, wherein determining the final set of edges ‘

X’

further comprising;

partitioning the final set of edges ‘

X’

into one or more sets ‘

X_i’

of edges; and

removing one or more incoming edges for the each node from X_i, the one or more incoming edges being removed when a number of the incoming edges for the each node of X_i is greater than ‘

K’

incoming edges.

9. The method of claim 1 further comprising computing a maximum probability edge from the initial candidate set of edges, the computed maximum probability edge being utilized in computing the gain corresponding to each edge of the one or more samples of edges.

10. The method of claim 1, wherein the probability value for the each edge of the one or more samples of edges is one of ‘

0’ and

‘

1’

.

11. A system for maximizing content spread in a social network, the social network comprising a set of nodes and a set of edges between one or more nodes of the set of nodes, the system comprising:

a functioning module configured to perform one or more functionalities to determine a subset of edges relevant for maximizing flow of a content in the social network, the functioning module comprising;

(a) a sampling module for generating one or more samples of edges from an initial candidate set of edges, each edge having a probability value for content flow thereto;

(b) a computing module for computing gain corresponding to each edge of the one or more samples of edges;

(c) a determining module configured to determine the subset of edges from the one or more samples of edges, the subset of edges being determined based on the computed gain, each node corresponding to each edge of the subset of edges having at least one of less than ‘

K’

incoming edges and equal to ‘

K’

incoming edges; and

(d) an incrementing module configured to increment the probability value of each edge of the subset of edges by a predefined value, the probability value of each edge of the subset of edges being incremented to upgrade the determined subset of edges,

wherein the functioning module performs one or more functionalities for a predefined number of iterations;

a rounding module configured to determine a final set of edges ‘

X’

from the upgraded subset of edges, the final set of edges ‘

X’

being determined by ensuring ‘

K’

incoming edges for each node of the upgraded set of edges; and

an output module configured to output the final set of edges ‘

X’

to maximize spreading of the content in the social network.

12. The system of claim 11, wherein each node of the set of nodes corresponding to a user of the social network.

13. The system of claim 11, wherein each edge of the set of edges corresponding to a connection between two users of the social network.

14. The system of claim 11 further comprising an identification module for identifying the initial candidate set of edges comprising candidate edges for a node, the initial candidate set being identified based on one or more characteristics corresponding to the node.

15. The system of claim 11, wherein the probability value for each edge is initialized as ‘

0’

.

16. The system of claim 11, wherein the determining module capable of determining the subset of edges from the one or more samples of edges when total gain for the subset of edges is maximum.

17. The system of claim 11, wherein ‘

K’

is a predefined number of incoming edges for the each node in the subset of edges.

18. The system of claim 11, wherein the rounding module further configured to:

perform partitioning of the final set of edges ‘

X’

into one or more sets ‘

X_i’

of edges; and

remove one or more incoming edges for the each node from X_i, the one or more incoming edges being removed when a number of the incoming edges for the each node of X_i is greater than ‘

K’

incoming edges.

19. The system of claim 11, wherein the computing module further configured to compute a maximum probability edge from the initial candidate set of edges, the computed maximum probability edge being utilized in computing the gain corresponding to each edge of the one or more samples of edges.

20. The system of claim 11, wherein the probability value for the each edge is one of ‘

0’ and

‘

1’

.

21. A computer program product for use with a computer, the computer program product comprising a non-transitory computer usable medium having a computer readable program code embodied therein for maximizing content spread in a social network, the computer readable program code when executed performing a method comprising:

executing steps (a) to (d) for performing one or more functionalities to determine a subset of edges relevant for maximizing flow of a content in the social network, the steps (a) to (d) comprising;

(a) generating one or more samples of edges from an initial candidate set of edges, each edge acquiring a probability value for content flow thereto;

(b) computing gain corresponding to each edge of the one or more samples of edges;

(c) determining the subset of edges from the one or more samples of edges, the subset of edges being determined based on the gain, each node corresponding to each edge of the subset of edges having at least one of less than ‘

K’

incoming edges and equal to ‘

K’

incoming edges; and

(d) incrementing the probability value of each edge of the subset of edges by a predefined value, the probability value of each edge of the subset of edges being incremented to upgrade the determined subset of edges,

wherein the steps (a) to (d) being performed for a predefined number of iterations;

determining a final set of edges ‘

X’

from the upgraded subset of edges, the final set of edges ‘

X’

being determined by ensuring ‘

K’

incoming edges for each node of the upgraded set of edges; and

outputting the final set of edges ‘

X’

to maximize spreading of the content in the social network.

22. The computer program product of claim 21, wherein each node of the set of nodes corresponding to a user of the social network.

23. The computer program product of claim 21, wherein each edge of the set of edges corresponding to a connection between two users of the social network.

24. The computer program product of claim 21, wherein the computer program code further identifies the initial candidate set of edges comprising candidate edges for a node, the initial candidate set being identified based on one or more characteristics corresponding to the node.

25. The computer program product of claim 21, wherein the probability value for each edge is initialized as ‘

0’

.

26. The computer program product of claim 21, wherein determining the subset of edges from the one or more samples of edges comprising determining one or more edges having maximum total gain.

27. The computer program product of claim 21, wherein ‘

K’

is a predefined number of incoming edges for the each node in the subset of edges.

28. The computer program product of claim 21, wherein determining the final set of edges ‘

X’

comprising;

partitioning the final set of edges ‘

X’

into one or more sets X_i of edges; and

removing one or more incoming edges for the each node from X_i, the one or more incoming edges being removed when a number of the incoming edges for the each node of X_i is greater than ‘

K’

incoming edges.

29. The computer program product of claim 21, wherein the computer program code further computes a maximum probability edge from the initial candidate set of edges, the computed maximum probability edge being utilized in computing the gain corresponding to each edge of the one or more samples of edges.

30. The computer program product of claim 21, wherein the probability value for the each edge of the one or more samples of edges is one of ‘

0’ and

‘

1’

.