Locality preserving dense graph convolutional networks with graph context-aware node representations.

Neural Networks : the Official Journal of the International Neural Network Society
Wenfeng LiuMingliang Xu

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 neighborhoods 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 s...Continue Reading

References

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May 4, 2020·Neural Networks : the Official Journal of the International Neural Network Society·M Tavassoli KejaniH Talebi
Jun 20, 2020·Neural Networks : the Official Journal of the International Neural Network Society·Davide BacciuMarco Podda
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Jul 19, 2020·Neural Networks : the Official Journal of the International Neural Network Society·Giannis NikolentzosMichalis Vazirgiannis

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