Co-clustering enterprise social networks

Ruiqi Hu, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
Subtitle of host publication24 - 29 July 2016 Vancouver, Canada
EditorsPablo A. Estevez
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509006199, 9781509006205
ISBN (Print)781509006212
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2016
Abbreviated titleIJCNN 2016
Internet address

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