Topology Adaptive Graph Convolutional Networks

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Convolution acts as a local feature extractor in convolutional neural networks (CNNs). However, the convolution operation is not applicable when the input data is supported on an irregular graph such as with social networks, citation networks, or knowledge graphs. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network that generalizes CNN architectures to graph-structured data and provides a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution, replacing the square filter for the grid-structured data in traditional CNNs. The outputs are the weighted sum of these filters’ outputs, extraction of both vertex features and strength of correlation between vertices. It can be used with both directed and undirected graphs. The proposed TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution in traditional signal processing. We apply TAGCN to semi-supervised learning problems for graph vertex classification; experiments on a number of data sets demonstrate that our method outperforms the existing graph convolutional neural networks and achieves state-of-the-art performance for each data set tested.
  • TL;DR: A systematic way to design a fixed-size learnable filters to extract feature from nodes and strength of correlation for both directed and undirected graph.
  • Keywords: graph convolutional neural networks, graph-structured data, semi-classification

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