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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.05670 |
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| _version_ | 1866913790634754048 |
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| author | Smith, John Tu, Wenxuan Wu, Junlong Zhang, Wenxin Liu, Jingxin Wang, Haotian Cheng, Jieren Lei, Huajie Yao, Guangzhen Wang, Lingren Li, Mengfei Han, Renda Li, Yu |
| author_facet | Smith, John Tu, Wenxuan Wu, Junlong Zhang, Wenxin Liu, Jingxin Wang, Haotian Cheng, Jieren Lei, Huajie Yao, Guangzhen Wang, Lingren Li, Mengfei Han, Renda Li, Yu |
| contents | Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notable advancements, yielding performance enhancements to a certain degree. However, existing methods suffer from at least one of the following issues: 1. the original graph structure has noise, and 2. during feature propagation and pooling processes, noise is gradually aggregated into the graph-level embeddings through information propagation. Consequently, these two limitations mask clustering-friendly information, leading to suboptimal graph-level clustering performance. To this end, we propose a novel Dual Boost-Driven Graph-Level Clustering Network (DBGCN) to alternately promote graph-level clustering and filtering out interference information in a unified framework. Specifically, in the pooling step, we evaluate the contribution of features at the global and optimize them using a learnable transformation matrix to obtain high-quality graph-level representation, such that the model's reasoning capability can be improved. Moreover, to enable reliable graph-level clustering, we first identify and suppress information detrimental to clustering by evaluating similarities between graph-level representations, providing more accurate guidance for multi-view fusion. Extensive experiments demonstrated that DBGCN outperforms the state-of-the-art graph-level clustering methods on six benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05670 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dual Boost-Driven Graph-Level Clustering Network Smith, John Tu, Wenxuan Wu, Junlong Zhang, Wenxin Liu, Jingxin Wang, Haotian Cheng, Jieren Lei, Huajie Yao, Guangzhen Wang, Lingren Li, Mengfei Han, Renda Li, Yu Machine Learning Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notable advancements, yielding performance enhancements to a certain degree. However, existing methods suffer from at least one of the following issues: 1. the original graph structure has noise, and 2. during feature propagation and pooling processes, noise is gradually aggregated into the graph-level embeddings through information propagation. Consequently, these two limitations mask clustering-friendly information, leading to suboptimal graph-level clustering performance. To this end, we propose a novel Dual Boost-Driven Graph-Level Clustering Network (DBGCN) to alternately promote graph-level clustering and filtering out interference information in a unified framework. Specifically, in the pooling step, we evaluate the contribution of features at the global and optimize them using a learnable transformation matrix to obtain high-quality graph-level representation, such that the model's reasoning capability can be improved. Moreover, to enable reliable graph-level clustering, we first identify and suppress information detrimental to clustering by evaluating similarities between graph-level representations, providing more accurate guidance for multi-view fusion. Extensive experiments demonstrated that DBGCN outperforms the state-of-the-art graph-level clustering methods on six benchmark datasets. |
| title | Dual Boost-Driven Graph-Level Clustering Network |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.05670 |