KDD Papers

PReP: Path-Based Relevance from a Probabilistic Perspective in Heteogeneous Information Networks

Yu Shi (Dept. of Computer Science, University of Illinois at Urbana-Champaign);Po-Wei Chan (Dept. of Computer Science, University of Illinois at Urbana-Champaign);Honglei Zhuang (Dept. of Computer Science, University of Illinois at Urbana-Champaign);Huan Gui (Dept. of Computer Science, University of Illinois at Urbana-Champaign);Jiawei Han (Dept. of Computer Science, University of Illinois at Urbana-Champaign)


Abstract

As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks. Inspired by the probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective. We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods. A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN. We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks. Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.


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