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CGC: Contrastive Graph Clustering forCommunity Detection and Tracking

Authors:

Iftikhar Ahamath Burhanuddin,

Christos FaloutsosAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1115 - 1126

https://doi.org/10.1145/3485447.3512160

Published: 25 April 2022 Publication History

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Abstract

Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clustering perspective. Recently, state-of-the-art clustering performance in various domains has been achieved by deep clustering methods. Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework. Despite some differences in modeling choices (e.g., encoder architectures), existing DGC methods are mainly based on autoencoders and use the same clustering objective with relatively minor adaptations. Also, while many real-world graphs are dynamic, previous DGC methods considered only static graphs. In this work, we develop CGC, a novel end-to-end framework for graph clustering, which fundamentally differs from existing methods. CGC learns node embeddings and cluster assignments in a contrastive graph learning framework, where positive and negative samples are carefully selected in a multi-level scheme such that they reflect hierarchical community structures and network homophily. Also, we extend CGC for time-evolving data, where temporal graph clustering is performed in an incremental learning fashion, with the ability to detect change points. Extensive evaluation on real-world graphs demonstrates that the proposed CGC consistently outperforms existing methods.

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Index Terms

CGC: Contrastive Graph Clustering forCommunity Detection and Tracking
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

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

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 25 April 2022

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Cited By

Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/366990618:8(1-44)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3669906
Wu XXiong KXiong YHe XZhang YJiao YZhang JBaeza-Yates RBonchi F(2024)ProCom: A Few-shot Targeted Community Detection AlgorithmProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671749(3414-3424)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671749
Sun LHu JZhou SHuang ZYe JPeng HYu ZYu PChua TNgo CKa-Wei Lee RKumar RLauw H(2024)RicciNet: Deep Clustering via A Riemannian Generative ModelProceedings of the ACM Web Conference 202410.1145/3589334.3645428(4071-4082)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645428
Zheng, ZShang YSun CChen J(2024)Deep graph clustering network by community prediction-guided augmentationThird International Symposium on Computer Applications and Information Systems (ISCAIS 2024)10.1117/12.3035234(142)Online publication date: 11-Jul-2024
https://doi.org/10.1117/12.3035234
Peng CKang KChen YKang ZChen CCheng Q(2024)Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral ClusteringIEEE Transactions on Image Processing10.1109/TIP.2024.338896933(3145-3160)Online publication date: 2024
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Wei XZhang ZHuang HZhou Y(2024)An overview on deep clusteringNeurocomputing10.1016/j.neucom.2024.127761590(127761)Online publication date: Jul-2024
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Shan ZZhang DLei Y(2024)Graph contrastive learning with cross-encoder for community discoveryApplied Intelligence10.1007/s10489-024-05287-354:2(2211-2224)Online publication date: 1-Feb-2024
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Zhuo MLiu YLiu LZhou S(2023)Local Cluster-Aware Attention for Non-Euclidean Structure DataSymmetry10.3390/sym1504083715:4(837)Online publication date: 31-Mar-2023
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