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Employees in companies can be divided into di erent communities, and thosewho frequently socialize with each other will be treated as close friends andare grouped in the same community. In the enterprise context, a large amount ofinformation about the employees is available in both (1) o ine company internalsources and (2) online enterprise social networks (ESNs). Each of theinformation sources also contain multiple categories of employees'socialization activities at the same time. In this paper, we propose to detectthe social communities of the employees in companies based on the broadlearning se ing with both these online and o ine information sourcessimultaneously, and the problem is formally called the "Broad Learning basedEnterprise Community Detection" (BL-Ecd) problem. To address the problem, anovel broad learning based community detection framework named "HeterogeneoUsMulti-sOurce ClusteRing" (Humor) is introduced in this paper. Based on thevarious enterprise social intimacy measures introduced in this paper, Humordetects a set of micro community structures of the employees based on each ofthe socialization activities respectively. To obtain the (globally) consistentcommunity structure of employees in the company, Humor further fuses thesemicro community structures via two broad learning phases: (1) intra-fusion ofmicro community structures to obtain the online and o ine (locally) consistentcommunities respectively, and (2) inter-fusion of the online and o inecommunities to achieve the (globally) consistent community structure ofemployees. Extensive experiments conducted on real-world enterprise datasetsdemonstrate our method can perform very well in addressing the BL-Ecd problem.

BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion