Mining Homophilic Groups of Users using Edge Attributed Node Embedding from Enterprise Social Networks.

We develop a method to identify groups of similarly behaving users with similar work contexts from their activity on enterprise social media. This would allow organizations to discover redundancies and increase efficiency. To better capture the network structure and communication characteristics, we model user communications with directed attributed edges in a graph. Communication parameters including engagement frequency, emotion words, and post lengths act as edge weights of the multiedge. Upon the resultant adjacency tensor, we develop a node embedding algorithm using higher order singular value tensor decomposition and convolutional autoencoder. We develop a peer group identification algorithm using the cluster labels obtained from the node embedding and show its results on Enron emails and StackExchange Workplace community. We observe that people of the same roles in enterprise social media are clustered together by our method. We provide a comparison with existing node embedding algorithms as a reference indicating that attributed social networks and our formulations are an efficient and scalable way to identify peer groups in an enterprise social network that aids in professional social matching.