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HowSim: A General and Effective Similarity Measure on Heterogeneous Information Networks

CategoryPublications 178

Authors: Yue Wang, Zhe Wang, Ziyuan Zhao, Zijian Li, Xun Jian, Lei Chen, Jianchun Song

Name of Conference: IEEE International Conference on Data Engineering(ICDE 2020), April 20-24, 2020, Dallas, Texas, USA

Date of Publication: April 20, 2020

Abstract

Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. Measuring the similarity among objects is an important task in data mining applications. Currently, several similarity measures are defined for HIN. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of the similarity scores. This makes the existing similarity measures difficult to use in real applications. To address this problem, we extend SimRank, a well-known similarity measure for homogeneous graphs, to HINs, by introducing the concept of decay graph. The newly proposed relevance measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, are demonstrated by extensive experiments.

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Keywords2020ICDESocial media marketingYue Wang Previous: Next: