Hard Sample Aware Network for Contrastive Deep Graph Clustering

Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, Cancan Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

90 引用 (Scopus)

摘要

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method. The source code of HSAN is shared at https://github.com/yueliu1999/HSAN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-DeepGraph-Clustering on Github.

源语言英语
主期刊名AAAI-23 Technical Tracks 7
编辑Brian Williams, Yiling Chen, Jennifer Neville
出版商AAAI press
8914-8922
页数9
ISBN(电子版)9781577358800
DOI
出版状态已出版 - 27 6月 2023
活动37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, 美国
期限: 7 2月 202314 2月 2023

出版系列

姓名Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
37

会议

会议37th AAAI Conference on Artificial Intelligence, AAAI 2023
国家/地区美国
Washington
时期7/02/2314/02/23

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