One Node One Model: Featuring the Missing-Half for Graph Clustering

• URL: http://arxiv.org/abs/2412.09902v2
• Date: Wed, 18 Dec 2024 04:51:33 GMT
• Title: One Node One Model: Featuring the Missing-Half for Graph Clustering
• Authors: Xuanting Xie, Bingheng Li, Erlin Pan, Zhaochen Guo, Zhao Kang, Wenyu Chen,
• Abstract summary: Feature selection in graph clustering is difficult because it requires simultaneously discovering clusters and identifying the relevant features for these clusters. We introduce a novel paradigm called one node one model", which builds an exclusive model for each node and defines the node label as a combination of predictions for node groups. Specifically, the proposed Feature Personalized Graph Clustering (FPGC)" method identifies cluster-relevant features for each node using a squeeze-and-excitation block, integrating these features into each model to form the final representations.
• Score: 10.316522132109354
• License:
• Abstract: Most existing graph clustering methods primarily focus on exploiting topological structure, often neglecting the ``missing-half" node feature information, especially how these features can enhance clustering performance. This issue is further compounded by the challenges associated with high-dimensional features. Feature selection in graph clustering is particularly difficult because it requires simultaneously discovering clusters and identifying the relevant features for these clusters. To address this gap, we introduce a novel paradigm called ``one node one model", which builds an exclusive model for each node and defines the node label as a combination of predictions for node groups. Specifically, the proposed ``Feature Personalized Graph Clustering (FPGC)" method identifies cluster-relevant features for each node using a squeeze-and-excitation block, integrating these features into each model to form the final representations. Additionally, the concept of feature cross is developed as a data augmentation technique to learn low-order feature interactions. Extensive experimental results demonstrate that FPGC outperforms state-of-the-art clustering methods. Moreover, the plug-and-play nature of our method provides a versatile solution to enhance GNN-based models from a feature perspective.

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