research-article

HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding

Authors:

Hao PengAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 639 - 648

https://doi.org/10.1145/3357384.3358061

Published: 03 November 2019 Publication History

Abstract

Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attention to the higher-order Markov chain nature of meta-path guided random walks, especially to the stationarity issue. In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process,and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph, and a meta-schema respectively. We conduct extensive experiments in several heterogeneous networks and demonstrate that our methods substantially outperform the existing state-of-the-art network embedding algorithms.

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HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding

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cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

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Published: 03 November 2019

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Cited By

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https://doi.org/10.1186/s12859-024-06028-6
Ammar KInoubli WZghal SNguifo E(2025)Navigating complexity: a comprehensive review of heterogeneous information networks and embedding techniquesKnowledge and Information Systems10.1007/s10115-025-02357-xOnline publication date: 13-Feb-2025
https://doi.org/10.1007/s10115-025-02357-x
Liu KKang MLi XDai W(2024)LHGCN: A Laminated Heterogeneous Graph Convolutional Network for Modeling User–Item Interaction in E-CommerceSymmetry10.3390/sym1612169516:12(1695)Online publication date: 21-Dec-2024
https://doi.org/10.3390/sym16121695
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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