Distant meta-path similarities for text-based heterogeneous information networks

Chenguang Wang, Yangqiu Song, Haoran Li, Yizhou Sun, Ming Zhang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Measuring network similarity is a fundamental data mining problem. The mainstream similarity measures mainly leverage the structural information regarding to the entities in the network without considering the network semantics. In the real world, the heterogeneous information networks (HINs) with rich semantics are ubiquitous. However, the existing network similarity doesn't generalize well in HINs because they fail to capture the HIN semantics. The meta-path has been proposed and demonstrated as a right way to represent semantics in HINs. Therefore, original meta-path based similarities (e.g., PathSim and KnowSim) have been successful in computing the entity proximity in HINs. The intuition is that the more instances of meta-path(s) between entities, the more similar the entities are. Thus the original meta-path similarity only applies to computing the proximity of two neighborhood (connected) entities. In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity. The main idea is that even there is no shared neighborhood entities of (i.e., no meta-path instances connecting) the two entities, but if the more similar neighborhood entities of the entities are, the more similar the two entities should be. We then find out the optimum distant meta-path similarity by exploring the similarity hypothesis space based on different theoretical foundations. We show the state-ofthe-art similarity performance of distant meta-path similarity on two text-based HINs and make the datasets public available.1

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450349185
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841


Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting


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