LSEGNN: Encode Local Topology Structure in Graph Neural Networks

Learning robust representations for nodes in graphs is crucial for graph learning tasks. Graph Neural Networks(GNNs) attract much attention recently as the frameworks achieve great success in node representation learning. Existing state-of-the-art GNN methods (like GCN) aggregate messages from neighbor nodes through message passing neural network to update representations for nodes. However, the message passing strategy fails to capture the structural similarity between nodes. Besides, it assumes that neighbor nodes are independent and ignores abundant local neighbor structures around nodes in real networks. This weakness may hurt the performance of GNNs in some classification tasks. To capture the overlooked information, in the experimental investigation, we found that co-occurrence probabilities based on random walks can preserve local neighbor structures among nodes well. Furthermore, we propose a novel but effective method to encode local structure information into node features by co-occurrence probabilities. We call this method Local Structure Enhanced Graph Neural Network, short as LSEGNN. Extensive experiments are conducted in benchmark datasets and the results show the effectiveness of our method.