Asymmetric Self-Supervised Graph Neural Networks

Although self-supervised learning (SSL) has been successfully applied to graph data using graph neural networks (GNNs), most of the existing methods only consider undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth between two connected nodes). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed graphs where information can only be passed along one direction. For example, a directed edge indicates that the information can only be conveyed forwardly from the start node to the end node, but not backwardly. To accommodate such an asymmetric structure of directed graphs, we propose a simple yet remarkably effective SSL framework for directed graph analysis to incorporate such one-way information passing. We define an incoming embedding and an outgoing embedding for each node to model its schemes of sending and receiving features respectively. We propose an auxiliary SSL task to predict the existence of the directed edges with the incoming and outgoing embeddings of nodes. The auxiliary SSL task is jointly trained with a downstream primary task that updates nodes’ incoming features and outgoing features in accordance with labels. Extensive experiments on multiple real-world directed graph datasets demonstrate outstanding performances of the proposed self-supervised GNNs in both node-level and graph-level tasks.