Auxiliary Graph for Attribute Graph Clustering |
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Authors: | Wang Li Siwei Wang Xifeng Guo Zhenyu Zhou En Zhu |
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Affiliation: | 1.School of Computer, National University of Defense Technology, Changsha 410000, China;2.School of Cyberspace Science, Dongguan University of Technology, Dongguan 523808, China |
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Abstract: | 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. |
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Keywords: | clustering auxiliary graph graph networks attribute graph |
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