Attention-driven Graph Clustering Network

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

42 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationMM '21
Subtitle of host publicationProceedings of the 29th ACM International Conference on Multimedia
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages935–943
ISBN (Electronic)978-1-4503-8651-7
Publication statusPublished - 2021

Publication series

NameMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia

Conference

Title29th ACM International Conference on Multimedia (MM 2021)
LocationHybrid (Onsite and Virtual)
PlaceChina
CityChengdu
Period20 - 24 October 2021

Abstract

The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature. However, the existing works (i) lack a flexible combination mechanism to adaptively fuse those two kinds of features for learning the discriminative representation and (ii) overlook the multi-scale information embedded at different layers for subsequent cluster assignment, leading to inferior clustering results. To this end, we propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN). Specifically, AGCN exploits a heterogeneity-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. Moreover, AGCN develops a scale-wise fusion module to adaptively aggregate the multi-scale features embedded at different layers. Based on a unified optimization framework, AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion. Compared with the existing deep clustering methods, our method is more flexible and effective since it comprehensively considers the numerous and discriminative information embedded in the network and directly produces the clustering results. Extensive quantitative and qualitative results on commonly used benchmark datasets validate that our AGCN consistently outperforms state-of-the-art methods.

Research Area(s)

  • attention-based mechanism, deep clustering, feature fusion, multi-scale features

Citation Format(s)

Attention-driven Graph Clustering Network. / Peng, Zhihao; Liu, Hui; Jia, Yuheng et al.
MM '21: Proceedings of the 29th ACM International Conference on Multimedia. New York: Association for Computing Machinery, 2021. p. 935–943 (MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review