Deep Attention-guided Graph Clustering with Dual Self-supervision
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Number of pages | 13 |
Journal / Publication | IEEE Transactions on Circuits and Systems for Video Technology |
Online published | 27 Dec 2022 |
Publication status | Online published - 27 Dec 2022 |
Link(s)
Abstract
Existing deep embedding clustering methods fail to sufficiently utilize the available off-the-shelf information from feature embeddings and cluster assignments, limiting their performance. To this end, we propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC). Specifically, DAGC first utilizes a heterogeneity-wise fusion module to adaptively integrate the features of the auto-encoder and the graph convolutional network in each layer and then uses a scale-wise fusion module to dynamically concatenate the multi-scale features in different layers. Such modules are capable of learning an informative feature embedding via an attention-based mechanism. In addition, we design a distribution-wise fusion module that leverages cluster assignments to acquire clustering results directly. To better explore the off-the-shelf information from the cluster assignments, we develop a dual self-supervision solution consisting of a soft self-supervision strategy with a Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Extensive experiments on nine benchmark datasets validate that our method consistently outperforms state-of-the-art methods. Especially, our method improves the ARI by more than 10.29% over the best baseline. The code will be publicly available at https://github.com/ZhihaoPENG-CityU/DAGC.
Research Area(s)
- Unsupervised learning, deep embedding clustering, feature fusion, self-supervision
Citation Format(s)
Deep Attention-guided Graph Clustering with Dual Self-supervision. / Peng, Zhihao; Liu, Hui; Jia, Yuheng et al.
In: IEEE Transactions on Circuits and Systems for Video Technology, 27.12.2022.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review