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Contrastive Cross-scale Graph Knowledge Synergy

Authors:

Irwin KingAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3422 - 3433

https://doi.org/10.1145/3580305.3599286

Published: 04 August 2023 Publication History

Abstract

Graph representation learning via Contrastive Learning (GCL) has drawn considerable attention recently. Efforts are mainly focused on gathering more global information via contrasting on a single high-level graph view, which, however, underestimates the inherent complex and hierarchical properties in many real-world networks, leading to sub-optimal embeddings. To incorporate these properties of a complex graph, we propose Cross-Scale Contrastive Graph Knowledge Synergy (CGKS), a generic feature learning framework, to advance graph contrastive learning with enhanced generalization ability and the awareness of latent anatomies. Specifically, to maintain the hierarchical information, we create a so-call graph pyramid (GP) consisting of coarse-grained graph views. Each graph view is obtained via the careful design topology-aware graph coarsening layer that extends the Laplacian Eigenmaps with negative sampling. To promote cross-scale information sharing and knowledge interactions among GP, we propose a novel joint optimization formula that contains a pairwise contrastive loss between any two coarse-grained graph views. This synergy loss not only promotes knowledge sharing that yields informative representations, but also stabilizes the training process. Experiments on various downstream tasks demonstrate the substantial improvements of the proposed method over its counterparts.

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Cited By

Zhang YZhu HSong ZChen YFu XMeng ZKoniusz PKing IBaeza-Yates RBonchi F(2024)Geometric View of Soft Decorrelation in Self-Supervised LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671914(4338-4349)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671914
Liu YZhang HHe TZheng TZhao J(2024)Bootstrap Latents of Nodes and Neighbors for Graph Self-supervised LearningMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70352-2_5(76-92)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70352-2_5
Song ZZhang YKing IOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Optimal block-wise asymmetric graph construction for graph-based semi-supervised learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669237(71135-71149)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669237
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Index Terms

Contrastive Cross-scale Graph Knowledge Synergy
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 04 August 2023

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CUHK 2410021, Research Impact Fund
National Key Research and Development Program of China

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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

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Cited By

Zhang YZhu HSong ZChen YFu XMeng ZKoniusz PKing IBaeza-Yates RBonchi F(2024)Geometric View of Soft Decorrelation in Self-Supervised LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671914(4338-4349)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671914
Liu YZhang HHe TZheng TZhao J(2024)Bootstrap Latents of Nodes and Neighbors for Graph Self-supervised LearningMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70352-2_5(76-92)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70352-2_5
Song ZZhang YKing IOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Optimal block-wise asymmetric graph construction for graph-based semi-supervised learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669237(71135-71149)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669237
Zhang YZhu HChen YSong ZKoniusz PKing IOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Mitigating the popularity bias of graph collaborative filteringProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669074(67533-67550)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669074
Song ZZhang YKing IOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)No change, no gainProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668180(47511-47526)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668180
Song ZZhang YKing IFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Towards Fair Financial Services for All: A Temporal GNN Approach for Individual Fairness on Transaction NetworksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615091(2331-2341)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615091
Wu YChen YYin ZDing WKing I(2023)A survey on graph embedding techniques for biomedical dataInformation Fusion10.1016/j.inffus.2023.101909100:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.101909

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