research-article

Mining heterogeneous information networks: a structural analysis approach

Authors:

Jiawei HanAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 14, Issue 2

Pages 20 - 28

https://doi.org/10.1145/2481244.2481248

Published: 30 April 2013 Publication History

Abstract

Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.

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Mining heterogeneous information networks: a structural analysis approach
1. Information systems

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 14, Issue 2

December 2012

81 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/2481244

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Copyright © 2013 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2013

Published in SIGKDD Volume 14, Issue 2

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Cited By

Liu FLi CWu B(2025)Community-oriented multi-scale heterogeneous community detection using weighted positives and debiased negativesKnowledge-Based Systems10.1016/j.knosys.2024.112934(112934)Online publication date: Jan-2025
https://doi.org/10.1016/j.knosys.2024.112934
Zeng JWang NLi J(2025)Knowledge-driven hierarchical intents modeling for recommendationExpert Systems with Applications10.1016/j.eswa.2024.125361259(125361)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125361
Ye HSun YGao YXu FQi J(2025)Heterogeneous Graph Transformer Auto-Encoder for multivariate time series forecastingComputers and Electrical Engineering10.1016/j.compeleceng.2024.109927122(109927)Online publication date: Mar-2025
https://doi.org/10.1016/j.compeleceng.2024.109927
Sun HKan ALiu JDu W(2025)HG-search: multi-stage search for heterogeneous graph neural networksApplied Intelligence10.1007/s10489-024-06058-w55:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10489-024-06058-w
Zheng YQu JYang J(2024)StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information NetworksApplied Sciences10.3390/app1402093514:2(935)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020935
Hang JHong ZFeng XWang GCao DQiao JWang HZhang D(2024)Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous GraphsProceedings of the VLDB Endowment10.14778/3685800.368582017:12(3973-3986)Online publication date: 8-Nov-2024
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Mei JSun SLi CXu CChen CLiu YWang JZhao CHou XGuo MHe BCong X(2024)FlowWalker: A Memory-Efficient and High-Performance GPU-Based Dynamic Graph Random Walk FrameworkProceedings of the VLDB Endowment10.14778/3659437.365943817:8(1788-1801)Online publication date: 1-Apr-2024
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Sun JDu SZhou JYuan XShen XLiang R(2024)Question Embedding on Weighted Heterogeneous Information Network for Knowledge TracingACM Transactions on Knowledge Discovery from Data10.1145/370315819:1(1-28)Online publication date: 4-Nov-2024
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Eytan LBogina VBen-Gal IKoenigstein N(2024)KPAR: Knowledge-aware Path-based Attentive Recommender with InterpretabilityACM Transactions on Recommender Systems10.1145/3673243Online publication date: 17-Jun-2024
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Lin TZhou CLi QHu D(2024)Multi-level Disentangled Contrastive Learning on Heterogeneous GraphsProceedings of the 2024 16th International Conference on Machine Learning and Computing10.1145/3651671.3651684(628-634)Online publication date: 2-Feb-2024
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