Auxiliary Graph for Attribute Graph Clustering
Attribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications. However, the presented topological structure emphasizes local links between linked...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/10/1409 |
| _version_ | 1797473419527192576 |
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| author | Wang Li Siwei Wang Xifeng Guo Zhenyu Zhou En Zhu |
| author_facet | Wang Li Siwei Wang Xifeng Guo Zhenyu Zhou En Zhu |
| author_sort | Wang Li |
| collection | DOAJ |
| description | Attribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications. However, the presented topological structure emphasizes local links between linked nodes but fails to convey relationships between nodes that are not directly linked, limiting the potential for future clustering performance improvement. To solve this issue, we offer the Auxiliary Graph for Attribute Graph Clustering technique (AGAGC). Specifically, we construct an additional graph as a supervisor based on the node attribute. The additional graph can serve as an auxiliary supervisor that aids the present one. To generate a trustworthy auxiliary graph, we offer a noise-filtering approach. Under the supervision of both the pre-defined graph and an auxiliary graph, a more effective clustering model is trained. Additionally, the embeddings of multiple layers are merged to improve the discriminative power of representations. We offer a clustering module for a self-supervisor to make the learned representation more clustering-aware. Finally, our model is trained using a triplet loss. Experiments are done on four available benchmark datasets, and the findings demonstrate that the proposed model outperforms or is comparable to state-of-the-art graph clustering models. |
| first_indexed | 2024-03-09T20:14:15Z |
| format | Article |
| id | doaj.art-1d4f277640cd48ef8dd6c30414d185e0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T20:14:15Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-1d4f277640cd48ef8dd6c30414d185e02023-11-24T00:03:13ZengMDPI AGEntropy1099-43002022-10-012410140910.3390/e24101409Auxiliary Graph for Attribute Graph ClusteringWang Li0Siwei Wang1Xifeng Guo2Zhenyu Zhou3En Zhu4School of Computer, National University of Defense Technology, Changsha 410000, ChinaSchool of Computer, National University of Defense Technology, Changsha 410000, ChinaSchool of Cyberspace Science, Dongguan University of Technology, Dongguan 523808, ChinaSchool of Computer, National University of Defense Technology, Changsha 410000, ChinaSchool of Computer, National University of Defense Technology, Changsha 410000, ChinaAttribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications. However, the presented topological structure emphasizes local links between linked nodes but fails to convey relationships between nodes that are not directly linked, limiting the potential for future clustering performance improvement. To solve this issue, we offer the Auxiliary Graph for Attribute Graph Clustering technique (AGAGC). Specifically, we construct an additional graph as a supervisor based on the node attribute. The additional graph can serve as an auxiliary supervisor that aids the present one. To generate a trustworthy auxiliary graph, we offer a noise-filtering approach. Under the supervision of both the pre-defined graph and an auxiliary graph, a more effective clustering model is trained. Additionally, the embeddings of multiple layers are merged to improve the discriminative power of representations. We offer a clustering module for a self-supervisor to make the learned representation more clustering-aware. Finally, our model is trained using a triplet loss. Experiments are done on four available benchmark datasets, and the findings demonstrate that the proposed model outperforms or is comparable to state-of-the-art graph clustering models.https://www.mdpi.com/1099-4300/24/10/1409clusteringauxiliary graphgraph networksattribute graph |
| spellingShingle | Wang Li Siwei Wang Xifeng Guo Zhenyu Zhou En Zhu Auxiliary Graph for Attribute Graph Clustering Entropy clustering auxiliary graph graph networks attribute graph |
| title | Auxiliary Graph for Attribute Graph Clustering |
| title_full | Auxiliary Graph for Attribute Graph Clustering |
| title_fullStr | Auxiliary Graph for Attribute Graph Clustering |
| title_full_unstemmed | Auxiliary Graph for Attribute Graph Clustering |
| title_short | Auxiliary Graph for Attribute Graph Clustering |
| title_sort | auxiliary graph for attribute graph clustering |
| topic | clustering auxiliary graph graph networks attribute graph |
| url | https://www.mdpi.com/1099-4300/24/10/1409 |
| work_keys_str_mv | AT wangli auxiliarygraphforattributegraphclustering AT siweiwang auxiliarygraphforattributegraphclustering AT xifengguo auxiliarygraphforattributegraphclustering AT zhenyuzhou auxiliarygraphforattributegraphclustering AT enzhu auxiliarygraphforattributegraphclustering |