SYSTEM AND METHOD OF DETERMINING ENTERPRISE SOCIAL NETWORK USAGE

• US 20130151429A1
• Filed: 11/29/2012
• Published: 06/13/2013
• Est. Priority Date: 11/30/2011
• Status: Abandoned Application

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Brian D. Friedman, Jin Cao, Li Erran Li
• Inventors: LI, Li Erran, CAO, Jin, GAO, Hongyu, FRIEDMAN, Brian D.
• Application Number:
  US13/688,885
• Publication Number:
  US 20130151429A1
• Time in Patent Office: Days
• US Class Current: 705/319
• CPC Class Codes: G06Q 10/105 Human resources
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

According to an embodiment, a computing system includes at least one computing device including a processor configured to use a logistic regression model to provide an indication of a relationship between a user'"'"'s position within an enterprise and how the user interacts with other users of an enterprise social network.

Claims

1. A computing system, comprising:

at least one computing device including a processor configured to use a logistic regression model to provide an indication of a relationship between a user'"'"'s position within an enterprise and how the user interacts with other users of an enterprise social network.

2. The computing system of claim 1, wherein the computing device is configured to use the logistic regression model to provide an indication corresponding to a quantified effect of the user'"'"'s position within the enterprise relative to other users on the user'"'"'s interactions through the enterprise social network.

3. The computing system of claim 1, wherein the computing device is configured to include as inputs

information regarding an enterprise organizational graph which indicates a hierarchical arrangement of individuals within the enterprise and

information regarding a user interaction graph which indicates at least one interaction between at least two users of the enterprise social network.

4. The computing system of claim 3, wherein the user interaction graph includes

a first node to represent the user;

a second node to represent another user of the enterprise social network; and

and an edge between the first and second nodes to represent at least one interaction between the represented users.

5. The computing system of claim 4, wherein the computing device is configured to treat the user interaction graph as an undirected graph.

6. The computing system of claim 3, wherein the user interaction graph is based on a selected amount of interaction data over a selected period of time.

7. The computing system of claim 3, wherein the user interaction graph comprises a random graph generated by a random process.

8. The computing system of claim 1, wherein the computing device is configured to model how a propensity of a connection between two users is affected by their mutual relationship in the enterprise hierarchy.

9. The computing system of claim 1, wherein the computing device is configured to use a statistical model that includes an indicator variable of a presence of an interaction between two users of the enterprise social network, the indicator variable being based on a probability of the interaction and wherein the probability is expressed as a function of at least one covariate derived from a hierarchical relationship between the users.

10. The computing system of claim 9, wherein the computing device is configured to use a logistic regression model, expressed as

11. A method of analyzing use of an enterprise social network, comprising the steps of:

providing computer executable instructions corresponding to a logistic regression model to at least one computing device including a processor configured to execute the instructions; and

using the at least one computing device to provide an indication of a relationship between a user'"'"'s hierarchical position within an enterprise and how the user interacts with other users of an enterprise social network based on the logistic regression model.

12. The method of claim 11, comprising using the logistic regression model to provide an indication corresponding to a quantified effect of the user'"'"'s position within the enterprise relative to other users on the user'"'"'s interactions through the enterprise social network.

13. The method of claim 12, comprising using

information regarding an enterprise organizational graph which indicates a hierarchical arrangement of individuals within the enterprise and

information regarding a user interaction graph which indicates at least one interaction between at least two users of the enterprise social network

as inputs for the logistic regression model.

14. The method of claim 13, wherein the user interaction graph is based on a selected amount of interaction data over a selected period of time.

15. The method of claim 13, wherein the user interaction graph comprises a random graph generated by a random process.

16. The method of claim 11, comprising modeling how a propensity of a connection between two users is affected by their mutual relationship in the enterprise hierarchy.

17. The method of claim 11, comprising using a statistical model that includes an indicator variable of a presence of an interaction between two users of the enterprise social network, the indicator variable being based on a probability of the interaction and wherein the probability is expressed as a function of at least one covariate derived from a hierarchical relationship between the users.

18. The method of claim 17, comprising using a logistic regression model, expressed as