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Residual Gated Graph ConvNets
Nov 03, 2017 (modified: Nov 03, 2017)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:Graph-structured data such as functional brain networks, social networks, gene regulatory networks, communications networks have brought the interest in generalizing neural networks to graph domains. In this paper, we are interested to design efficient neural network architectures for graphs with variable length. Several existing works such as Scarselli et al. (2009); Li et al. (2016) have focused on recurrent neural networks (RNNs) to solve this task. A recent different approach was proposed in Sukhbaatar et al. (2016), where a vanilla graph convolutional neural network (ConvNets) was introduced. We believe the latter approach to be a better paradigm to solve graph learning problems because ConvNets are more pruned to deep networks than RNNs. For this reason, we propose the most generic class of residual multi-layer graph ConvNets that make use of an edge gating mechanism, as proposed in Marcheggiani & Titov (2017). Gated edges appear to be a natural property in the context of graph learning tasks, as the system has the ability to learn which edges are important or not for the task to solve. We apply several graph neural models to two basic network science tasks; subgraph matching and semi-supervised clustering for graphs with variable length. Numerical results show the performances of the new model.
TL;DR:We propose a generic class of graph ConvNets with edge gating mechanism and residual formulation.