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Graph-structured data such as social networks, functional brain networks,gene regulatory networks, communications networks have brought the interest ingeneralizing deep learning techniques to graph domains. In this paper, we areinterested to design neural networks for graphs with variable length in orderto solve learning problems such as vertex classification, graph classification,graph regression, and graph generative tasks. Most existing works have focusedon recurrent neural networks (RNNs) to learn meaningful representations ofgraphs, and more recently new convolutional neural networks (ConvNets) havebeen introduced. In this work, we want to compare rigorously these twofundamental families of architectures to solve graph learning tasks. We reviewexisting graph RNN and ConvNet architectures, and propose natural extension ofLSTM and ConvNet to graphs with arbitrary size. Then, we design a set ofanalytically controlled experiments on two basic graph problems, i.e. subgraphmatching and graph clustering, to test the different architectures. Numericalresults show that the proposed graph ConvNets are 3-17% more accurate and1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate thanvariational (non-learning) techniques. Finally, the most effective graphConvNet architecture uses gated edges and residuality. Residuality plays anessential role to learn multi-layer architectures as they provide a 10% gain ofperformance.

Residual Gated Graph ConvNets