WGCN: Graph Convolutional Networks with Weighted Structural Features

• URL: http://arxiv.org/abs/2104.14060v1
• Date: Thu, 29 Apr 2021 00:50:06 GMT
• Title: WGCN: Graph Convolutional Networks with Weighted Structural Features
• Authors: Yunxiang Zhao and Jianzhong Qi and Qingwei Liu and Rui Zhang
• Abstract summary: Graph convolutional networks (GCNs) learn nodes' representations by capturing structural information. We propose a GCN model with weighted structural features named WGCN. Experiments show that WGCN outperforms the baseline models consistently by up to 17.07% in terms of accuracy on five benchmark datasets.
• Score: 25.64794159605137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph structural information such as topologies or connectivities provides valuable guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing GCN models that capture nodes' structural information weight in- and out-neighbors equally or differentiate in- and out-neighbors globally without considering nodes' local topologies. We observe that in- and out-neighbors contribute differently for nodes with different local topologies. To explore the directional structural information for different nodes, we propose a GCN model with weighted structural features, named WGCN. WGCN first captures nodes' structural fingerprints via a direction and degree aware Random Walk with Restart algorithm, where the walk is guided by both edge direction and nodes' in- and out-degrees. Then, the interactions between nodes' structural fingerprints are used as the weighted node structural features. To further capture nodes' high-order dependencies and graph geometry, WGCN embeds graphs into a latent space to obtain nodes' latent neighbors and geometrical relationships. Based on nodes' geometrical relationships in the latent space, WGCN differentiates latent, in-, and out-neighbors with an attention-based geometrical aggregation. Experiments on transductive node classification tasks show that WGCN outperforms the baseline models consistently by up to 17.07% in terms of accuracy on five benchmark datasets.

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