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Co-Evolution of Multi-Typed Objects in Dynamic Star Networks

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

Published 2014 in IEEE Transactions on Knowledge and Data Engineering

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    IEEE Transactions on Knowledge and Data Engineering

  • Publication date

    2014-12-01

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    DOI 10.1109/TKDE.2013.103
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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