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[期刊论文]

Towards Adaptive Masked Structural Learning for Graph-Level Clustering

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author:

Yang, J. (Yang, J..) [1] | Cai, J. (Cai, J..) [2] | Zhang, Y. (Zhang, Y..) [3] | Unfold

Indexed by:

Scopus

Abstract:

Graph data presents a vast landscape for real-world applications. Current graph-level clustering approaches predominantly utilize graph neural networks to capture the intricate structural information for graph data. However, a significant challenge arises in effectively integrate structural and feature information under the prevalent noise in the real-world scenario. The advent of masking strategies has marked significant strides in boosting model robustness, accommodating incomplete data, and enhancing generalization capabilities. Yet, research attention on leveraging mask strategy for facilitating graph-level clustering is still limited. In this paper, we introduce a novel graph-level clustering method, towards adaptive masked structural learning for graph-level clustering. The method performs adaptive masking through reconstruction loss, and jointly adaptive mask representation learning and clustering in an end-to-end unsupervised framework. The mutual information between maximized the entire graph and substructure representations is also utilized to learn to generate cluster-oriented graph-level representations. Extensive experiments on eight real graph-level benchmark datasets demonstrate the effectiveness and superiority of the proposed method.  © 2013 IEEE.

Keyword:

deep learning Graph clustering graph neural networks unsupervised learning

Community:

  • [ 1 ] [Yang J.]Fuzhou University, College of Computer and Data Science, Fuzhou, 350116, China
  • [ 2 ] [Yang J.]Bank of Quanzhou Co., Ltd., Quanzhou, 362046, China
  • [ 3 ] [Cai J.]National University of Singapore, Institute of Data Science, Singapore, 117602, Singapore
  • [ 4 ] [Zhang Y.]Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
  • [ 5 ] [Huang S.]Fuzhou University, College of Computer and Data Science, Fuzhou, 350116, China
  • [ 6 ] [Wang S.]Fuzhou University, College of Computer and Data Science, Fuzhou, 350116, China
  • [ 7 ] [Wang S.]Chinese University of Hon g Kong, Guangdong Provincial Key Laboratory of Big Data Computing, Shenzhen, 518172, China

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Source :

IEEE Transactions on Network Science and Engineering

ISSN: 2327-4697

Year: 2025

6 . 7 0 0

JCR@2023

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

30 Days PV: 0

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