Co-evolution of multi-typed objects in dynamic star networks

Yizhou Sun, Jie Tang, Jiawei Han, Cheng Chen, Manish Gupta

Research output: Contribution to journalArticlepeer-review


Mining network evolution has emerged as an intriguing research topic in many domains such as data mining, social networks, and machine learning. While a bulk of research has focused on mining evolutionary patterns of homogeneous networks (e.g., networks of friends), however, most real-world networks are heterogeneous, containing objects of different types, such as authors, papers, venues, and terms in a bibliographic network. Modeling co-evolution of multityped objects can capture richer information than that on single-typed objects alone. For example, studying co-evolution of authors, venues, and terms in a bibliographic network can tell better the evolution of research areas than just examining co-author network or term network alone. In this paper, we study mining co-evolution of multityped objects in a special type of heterogeneous networks, called star networks, and examine how the multityped objects influence each other in the network evolution. A hierarchical Dirichlet process mixture model-based evolution model is proposed, which detects the co-evolution of multityped objects in the form of multityped cluster evolution in dynamic star networks. An efficient inference algorithm is provided to learn the proposed model. Experiments on several real networks (DBLP, Twitter, and Delicious) validate the effectiveness of the model and the scalability of the algorithm.

Original languageEnglish (US)
Article number6945935
Pages (from-to)2942-2955
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
StatePublished - Dec 1 2014


  • Data mining
  • Database Applications
  • Database Management
  • Information Technology and Systems

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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