  Employees in companies can be divided into di erent communities, and those
who frequently socialize with each other will be treated as close friends and
are grouped in the same community. In the enterprise context, a large amount of
information about the employees is available in both (1) o ine company internal
sources and (2) online enterprise social networks (ESNs). Each of the
information sources also contain multiple categories of employees'
socialization activities at the same time. In this paper, we propose to detect
the social communities of the employees in companies based on the broad
learning se ing with both these online and o ine information sources
simultaneously, and the problem is formally called the "Broad Learning based
Enterprise Community Detection" (BL-Ecd) problem. To address the problem, a
novel broad learning based community detection framework named "HeterogeneoUs
Multi-sOurce 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 each of
the socialization activities respectively. To obtain the (globally) consistent
community structure of employees in the company, Humor further fuses these
micro community structures via two broad learning phases: (1) intra-fusion of
micro community structures to obtain the online and o ine (locally) consistent
communities respectively, and (2) inter-fusion of the online and o ine
communities to achieve the (globally) consistent community structure of
employees. Extensive experiments conducted on real-world enterprise datasets
demonstrate our method can perform very well in addressing the BL-Ecd problem.
