An Experimental Study of Neural Networks for Variable Graphs


Graph-structured data such as social networks, functional brain networks, chemical molecules have brought the interest in generalizing deep learning techniques to graph domains. In this work, we propose an empirical study of neural networks for graphs with variable size and connectivity. We rigorously compare several graph recurrent neural networks (RNNs) and graph convolutional neural networks (ConvNets) to solve two fundamental and representative graph problems, subgraph matching and graph clustering. Numerical results show that graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs. Interestingly, graph ConvNets are also 36% more accurate than non-learning (variational) techniques. The benefit of such study is to show that complex architectures like LSTM is not useful in the context of graph neural networks, but one should favour architectures with minimal inner structures, such as locality, weight sharing, index invariance, multi-scale, gates and residuality, to design efficient novel neural network models for applications like drugs design, genes analysis and particle physics.

ICLR 2018 Workshop
Xavier Bresson
Assoc. Professor of Computer Science

Xavier Bresson is Associate Professor in Computer Science at NTU, Singapore. He is a leading researcher in the field of Graph Deep Learning, a new framework that combines graph theory and deep learning techniques.