SOCIAL ACTION AND SOCIAL TIE PREDICTION

1 . A computing device comprising:

a processor to:

access social network data associated with a set of users of a social network;

parse social action data and social tie data from the social network data;

calculate a set of parameters defining relationships between the social action data and the social tie data; and

determine, using the set of parameters, a social action and a social tie associated with a particular user of the social network.

2 . The computing device of claim 1 , wherein the social tie data specifies particular types of connections between users in the set of users.

3 . The computing device of claim 1 , wherein the social action data specifies characteristics, preferences, or actions associated with users in the set of users.

4 . The computing device of claim 1 , wherein the processor is further to provide relevant information to the particular user based on the social action and the social tie.

5 . The computing device of claim 1 , wherein the processor is further to provide relevant information to a friend of the particular user based on the social action and the social tie.

6 . The computing device of claim 1 , wherein the processor is further to determine social actions and social ties for a plurality of users being different from the set of users.

7 . The computing device of claim 1 , wherein the processor is further to:

specify distributions associated with the relationships between the social action data and the social tie data;

generate a mutual latent random graph based on the distributions; and

using the mutual latent random graph, determine at least one latent factor associated with the relationships between the social action data and the social tie data, the set of parameters being calculated based on the mutual latent graph and the at least one latent factor.

8 . The computing device of claim 1 , wherein the set of parameters includes parameters associated with the social action data, parameters associated with social tie data, and parameters associated with the relationships between the social action data and the social tie data.

9 . The computing device of claim 1 , wherein the processor is further to optimize the set of parameters based on a mutual gradient descent algorithm.

10 . A method comprising:

receiving, by a computing device, social network data associated with a set of users of a social network;

extracting, by the computing device, social action data and social tie data from the social network data;

generating, by the computing device, a set of parameters defining relationships between the social action data and the social tie data; and

predicting, by the computing device, a social action and a social tie associated with a particular user of the social network including predicting the social action and the social tie using the set of parameters.

11 . The method of claim 10 , wherein the social tie data specifies particular types of connections between users in the set of users.

12 . The method of claim 10 , wherein the social action data specifies characteristics, preferences, or actions associated with users in the set of users.

13 . The method of claim 10 , further comprising:

providing relevant information to the particular user based on the social action and the social tie.

14 . The method of claim 10 , further comprising:

providing relevant information to a friend of the particular user based on the social action and the social tie.

15 . The method of claim 10 , further comprising:

specifying distributions associated with the relationships between the social action data and the social tie data;

generating a mutual latent random graph based on the distributions; and

using the mutual latent random graph, determining at least one latent factor associated with the relationships between the social action data and the social tie data, the set of parameters being generated based on the mutual latent random graph and the at least one latent factor.

16 . A non-transitory machine-readable storage medium storing instructions that, if executed by at least one processor of a computing device, cause the computing device to:

access social network data associated with a set of users of a social network;

extract social action data and social tie data from the social network data;

determine relationships between the social action data and the social tie data;

optimize a set of parameters defining the relationships between the social action data and the social tie data; and

predict, using the set of parameters, a social action and a social tie associated with a particular user of the social network.

17 . The non-transitory machine-readable storage medium of claim 16 , wherein the social tie data specifies particular types of connections between users in the set of users.

18 . The non-transitory machine-readable storage medium of claim 16 , wherein the social action data specifies characteristics, preferences, or actions associated with users in the set of users.

19 . The non-transitory machine-readable storage medium of claim 16 , wherein the instructions, if executed by the at least one processor, further cause the computing device to provide relevant information to the particular user based on the social action and the social tie.

20 . The non-transitory machine-readable storage medium of claim 16 , wherein the instructions, if executed by the at least one processor, further cause the computing device to provide relevant information to a friend of the particular user based on the social action and the social tie.