SYSTEM AND METHOD OF DETERMINING ENTERPRISE SOCIAL NETWORK USAGE
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.
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
log
it
(
p
ij
)
=
log
p
ij
1
-
p
ij
=
μ
+
α
T
Z
ij
+
β
T
X
ij
,
wherein μ, α, β are vectors estimated from interaction data corresponding to use of the enterprise social network, T stands for transpose, X ij are covariates related to an organization graph indicating the hierarchical relationship, and Z ij are exogenous covariates.
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
log
it
(
p
ij
)
=
log
p
ij
1
-
p
ij
=
μ
+
α
T
Z
ij
+
β
T
X
ij
,
wherein μ, α, β are vectors estimated from interaction data corresponding to use of the enterprise social network, T stands for transpose, X ij are covariates related to an organization graph indicating the hierarchical relationship, and Z ij are exogenous covariates.