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metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Authors:

Nitesh V. Chawla,

Ananthram SwamiAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 135 - 144

https://doi.org/10.1145/3097983.3098036

Published: 04 August 2017 Publication History

Abstract

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.

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Cited By

Xi XYuan JLu SHe J(2025)Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive LearningTsinghua Science and Technology10.26599/TST.2023.901014930:1(215-233)Online publication date: Mar-2025
https://doi.org/10.26599/TST.2023.9010149
Zhao HRui PChen JZhao SZhang Y(2025)Restage: Relation Structure-Aware Hierarchical Heterogeneous Graph EmbeddingTsinghua Science and Technology10.26599/TST.2023.901014730:1(198-214)Online publication date: Mar-2025
https://doi.org/10.26599/TST.2023.9010147
Xiong XZhang XJiang WLiu TLiu YLiu L(2025)Lightweight Dual-Stream SAR–ATR Framework Based on an Attention Mechanism-Guided Heterogeneous Graph NetworkIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.349832718(537-556)Online publication date: 2025
https://doi.org/10.1109/JSTARS.2024.3498327
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Index Terms

metapath2vec: Scalable Representation Learning for Heterogeneous Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
    2. Machine learning approaches
      1. Learning latent representations
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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Published: 04 August 2017

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KDD '17

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

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Cited By

Xi XYuan JLu SHe J(2025)Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive LearningTsinghua Science and Technology10.26599/TST.2023.901014930:1(215-233)Online publication date: Mar-2025
https://doi.org/10.26599/TST.2023.9010149
Zhao HRui PChen JZhao SZhang Y(2025)Restage: Relation Structure-Aware Hierarchical Heterogeneous Graph EmbeddingTsinghua Science and Technology10.26599/TST.2023.901014730:1(198-214)Online publication date: Mar-2025
https://doi.org/10.26599/TST.2023.9010147
Xiong XZhang XJiang WLiu TLiu YLiu L(2025)Lightweight Dual-Stream SAR–ATR Framework Based on an Attention Mechanism-Guided Heterogeneous Graph NetworkIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.349832718(537-556)Online publication date: 2025
https://doi.org/10.1109/JSTARS.2024.3498327
Tang RYong ZJiang SChen XLiu YZhang YSun GWang W(2025)Network alignmentPhysics Reports10.1016/j.physrep.2024.11.0061107(1-45)Online publication date: Mar-2025
https://doi.org/10.1016/j.physrep.2024.11.006
Guo XLi JJiao PZhang WLi TWang W(2025)Counterfactual learning for higher-order relation prediction in heterogeneous information networksNeural Networks10.1016/j.neunet.2024.107024183(107024)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2024.107024
Sun YZhu DWang YFu YTian Z(2025)GTC: GNN-Transformer co-contrastive learning for self-supervised heterogeneous graph representationNeural Networks10.1016/j.neunet.2024.106645181(106645)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106645
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Amara AHadj Taieb MBen Aouicha M(2025)A multi-view GNN-based network representation learning framework for recommendation systemsNeurocomputing10.1016/j.neucom.2024.129001619(129001)Online publication date: Mar-2025
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Wu HZhang MXiang ZChen YYu FXia XLi Y(2025)Multi-level discriminator based contrastive learning for multiplex networksNeurocomputing10.1016/j.neucom.2024.128754613(128754)Online publication date: Jan-2025
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Li JGuo XJiao PWang W(2025)Learning accurate neighborhood- and self-information for higher-order relation prediction in Heterogeneous Information NetworksNeurocomputing10.1016/j.neucom.2024.128739613(128739)Online publication date: Jan-2025
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