Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations

• URL: http://arxiv.org/abs/2010.05404v1
• Date: Mon, 12 Oct 2020 02:12:27 GMT
• Title: Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
• Authors: Wenfeng Liu, Maoguo Gong, Zedong Tang, A. K. Qin
• Abstract summary: Graph convolutional networks (GCNs) have been widely used for representation learning on graph data. In many graph classification applications, GCN-based approaches have outperformed traditional methods. We propose a locality-preserving dense GCN with graph context-aware node representations.
• Score: 19.623379678611744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed convolution and readout operations. In many graph classification applications, GCN-based approaches have outperformed traditional methods. However, most of the existing GCNs are inefficient to preserve local information of graphs -- a limitation that is especially problematic for graph classification. In this work, we propose a locality-preserving dense GCN with graph context-aware node representations. Specifically, our proposed model incorporates a local node feature reconstruction module to preserve initial node features into node representations, which is realized via a simple but effective encoder-decoder mechanism. To capture local structural patterns in neighbourhoods representing different ranges of locality, dense connectivity is introduced to connect each convolutional layer and its corresponding readout with all previous convolutional layers. To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer's readout to form a global context-aware node representation. In addition, a self-attention module is introduced to aggregate layer-wise representations to form the final representation. Experiments on benchmark datasets demonstrate the superiority of the proposed model over state-of-the-art methods in terms of classification accuracy.

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