Article

Attributed graph clustering via adaptive graph convolution

Authors:

Xiaotong Zhang,

Xiao-Ming WuAuthors Info & Claims

IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence

Pages 4327 - 4333

Published: 10 August 2019 Publication History

Abstract

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution of a fixed and low order that only takes into account neighbours within a few hops of each node, which underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. We establish the validity of our method by theoretical analysis and extensive experiments on benchmark datasets. Empirical results show that our method compares favourably with state-of-the-art methods.

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Cited By

Yang XMin ELiang KLiu YWang SZhou SWu HLiu XZhu ECai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)GraphLearner: Graph Node Clustering with Fully Learnable AugmentationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680602(5517-5526)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680602
Liu YHe TWu QZheng TZhao JSerra ESpezzano F(2024)Scalable and Adaptive Spectral Embedding for Attributed Graph ClusteringProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679992(3912-3916)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679992
Hu FDeng ZWang GXie ZChoi KWang S(2024)Graph Fuzzy System for the Whole Graph Prediction: Concepts, Models, and AlgorithmsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.332545832:3(1383-1398)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3325458
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Attributed graph clustering via adaptive graph convolution
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Published In

cover image Guide Proceedings

IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence

August 2019

6589 pages

ISBN:9780999241141

Editor:
Sarit Kraus
Bar-Ilan University (ISRAEL)

Sponsors

Sony: Sony Corporation
Huawei Technologies Co. Ltd.: Huawei Technologies Co. Ltd.
Baidu Research: Baidu Research
The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)
Lenovo: Lenovo

Publisher

AAAI Press

Publication History

Published: 10 August 2019

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Cited By

Yang XMin ELiang KLiu YWang SZhou SWu HLiu XZhu ECai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)GraphLearner: Graph Node Clustering with Fully Learnable AugmentationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680602(5517-5526)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680602
Liu YHe TWu QZheng TZhao JSerra ESpezzano F(2024)Scalable and Adaptive Spectral Embedding for Attributed Graph ClusteringProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679992(3912-3916)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679992
Hu FDeng ZWang GXie ZChoi KWang S(2024)Graph Fuzzy System for the Whole Graph Prediction: Concepts, Models, and AlgorithmsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.332545832:3(1383-1398)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3325458
Zhu YXu YYu FLiu QWu S(2023)Unsupervised Graph Representation Learning with Cluster-aware Self-training and RefiningACM Transactions on Intelligent Systems and Technology10.1145/360848014:5(1-21)Online publication date: 11-Aug-2023
https://dl.acm.org/doi/10.1145/3608480
Su SGuan JChen BHuang X(2023)Nonnegative Matrix Factorization Based on Node Centrality for Community DetectionACM Transactions on Knowledge Discovery from Data10.1145/357852017:6(1-21)Online publication date: 28-Feb-2023
https://dl.acm.org/doi/10.1145/3578520
Wang MLi BHe KHopcroft J(2023)Uncovering the Local Hidden Community Structure in Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/356759717:5(1-25)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3567597
Conti MLi JPicek SXu JDemontis AChen XTramèr F(2022)Label-Only Membership Inference Attack against Node-Level Graph Neural NetworksProceedings of the 15th ACM Workshop on Artificial Intelligence and Security10.1145/3560830.3563734(1-12)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3560830.3563734
Huang SLiu YRen YTsang IXu ZLv JMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Learning Smooth Representation for Multi-view Subspace ClusteringProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548248(3421-3429)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548248
Fanseu Kamhoua BZhang LMa KCheng JLi BHan BDemartini GZuccon GCulpepper JHuang ZTong H(2021)HyperGraph Convolution Based Attributed HyperGraph ClusteringProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482437(453-463)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482437

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