LGL-GNN: Learning Global and Local Information for Graph Neural Networks

Abstract

In this article, we have developed a graph convolutional network model LGL that can learn global and local information at the same time for effective graph classification tasks. Our idea is to concatenate the convolution results of the deep graph convolutional network and the motif-based subgraph convolutional network layer by layer, and give attention weights to global features and local features. We hope that this method can alleviate the over-smoothing problem when the depth of the neural networks increases, and the introduction of motif for local convolution can better learn local neighborhood features with strong connectivity. Finally, our experiments on standard graph classification benchmarks prove the effectiveness of the model.