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Graph Communal Contrastive Learning

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Hanghang TongAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1203 - 1213

https://doi.org/10.1145/3485447.3512208

Published: 25 April 2022 Publication History

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Abstract

Graph representation learning is crucial for many real-world applications (e.g. social relation analysis). A fundamental problem for graph representation learning is how to effectively learn representations without human labeling, which is usually costly and time-consuming. Graph contrastive learning (GCL) addresses this problem by pulling the positive node pairs (or similar nodes) closer while pushing the negative node pairs (or dissimilar nodes) apart in the representation space. Despite the success of the existing GCL methods, they primarily sample node pairs based on the node-level proximity yet the community structures have rarely been taken into consideration. As a result, two nodes from the same community might be sampled as a negative pair. We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar. To address this issue, we propose a novel Graph Communal Contrastive Learning (gCooL) framework to jointly learn the community partition and learn node representations in an end-to-end fashion. Specifically, the proposed gCooL consists of two components: a Dense Community Aggregation (DeCA) algorithm for community detection and a Reweighted Self-supervised Cross-contrastive (ReSC) training scheme to utilize the community information. Additionally, the real-world graphs are complex and often consist of multiple views. In this paper, we demonstrate that the proposed gCooL can also be naturally adapted to multiplex graphs. Finally, we comprehensively evaluate the proposed gCooL on a variety of real-world graphs. The experimental results show that the gCooL outperforms the state-of-the-art methods.

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Cited By

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Graph Communal Contrastive Learning

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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NSF Program on Fairness in AI in collaboration with Amazon
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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

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https://dl.acm.org/doi/10.1145/3638054
Tang JYang YWei WShi LSu LCheng SYin DHuang CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GraphGPT: Graph Instruction Tuning for Large Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657775(491-500)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657775
Lee SLee SLee JLee WSon Y(2024)Graph contrastive learning with consistency regularizationPattern Recognition Letters10.1016/j.patrec.2024.03.014181(43-49)Online publication date: May-2024
https://doi.org/10.1016/j.patrec.2024.03.014
Bu WCao XZheng YPan S(2024)Improving Augmentation Consistency for Graph Contrastive LearningPattern Recognition10.1016/j.patcog.2023.110182148:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.110182
Xiang NYou MWang QTian B(2024)Hypergraph network embedding for community detectionThe Journal of Supercomputing10.1007/s11227-024-06003-180:10(14180-14202)Online publication date: 16-Mar-2024
https://dl.acm.org/doi/10.1007/s11227-024-06003-1
Huang ZXu WZhuo X(2023)Community-CL: An Enhanced Community Detection Algorithm Based on Contrastive LearningEntropy10.3390/e2506086425:6(864)Online publication date: 29-May-2023
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Zhang YChen YSong ZKing ISingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Contrastive Cross-scale Graph Knowledge SynergyProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599286(3422-3433)Online publication date: 6-Aug-2023
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