Clustering social audiences in business information networks

Yu Zheng, Ruiqi Hu, Sai-fu Fung, Celina Yu, Guodong Long, Ting Guo, Shirui Pan

Research output: Contribution to journalArticleResearchpeer-review


Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.

Original languageEnglish
Article number107126
Number of pages12
JournalPattern Recognition
Publication statusPublished - Apr 2020


  • Business information networks
  • Clustering
  • Machine learning
  • Social networks

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