Predicting links in multi-relational and heterogeneous networks

Yang Yang, Nitesh Chawla, Yizhou Sun, Jiawei Hani

Research output: Chapter in Book/Entry/PoemConference contribution

93 Scopus citations


Link prediction is an important task in network analysis, benefiting researchers and organizations in a variety of fields. Many networks in the real world, for example social networks, are heterogeneous, having multiple types of links and complex dependency structures. Link prediction in such networks must model the influence propagating between heterogeneous relationships to achieve better link prediction performance than in homogeneous networks. In this paper, we introduce Multi-Relational Influence Propagation (MRIP), a novel probabilistic method for heterogeneous networks. We demonstrate that MRIP is useful for predicting links in sparse networks, which present a significant challenge due to the severe disproportion of the number of potential links to the number of real formed links. We also explore some factors that can inform the task of classification yet remain unexplored, such as temporal information. In this paper we make use of the temporal-related features by carefully investigating the issues of feasibility and generality. In accordance with our work in unsupervised learning, we further design an appropriate supervised approach in heterogeneous networks. Our experiments on co-authorship prediction demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Number of pages10
StatePublished - 2012
Externally publishedYes
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference12th IEEE International Conference on Data Mining, ICDM 2012

ASJC Scopus subject areas

  • General Engineering


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