Enterprise Community Detection

Employees in companies can be divided into different social communities, and those who frequently socialize with each other are treated as close friends and will be grouped in the same community. In the enterprise context, a large amount of information about the employees is available in both (1) offline company internal sources and (2) online enterprise social networks (ESNs). What's more, each of the information sources can also contain multiple categories of employees' socialization activity information at the same time. In this paper, we propose to detect the social communities of the employees in companies based on these different information sources simultaneously, and the problem is formally called the "Enterprise Community Detection" (ECD) problem. To address the problem, a novel community detection framework named "HeterogeneoUs MultisOurce ClusteRing" (HUMOR) is introduced in this paper. Based on the various enterprise social intimacy measures introduced in this paper, HUMOR detects a set of micro community structures of the employees based on these different categories of information available in the online and offline sources respectively. (A full version of this paper is available in [5]).