Pareto Graph Self-Supervised Learning

Abstract

In this paper, we study the problem of finding proper tradeoff for graph self-supervised learning. Recently, various self-supervised auxiliary tasks have been proposed to accelerate representation learning in Graph Neural Networks (GNNs). However, existing graph self-supervised learning ignores the task conflicts between the main task and auxiliary tasks. In this paper, we propose Pareto graph self-supervised learning, a general learning framework that not only finds the solution where the main task rather than auxiliary tasks achieves the best performance in graph self-supervised learning, but more importantly, learns the personalized self-supervised task for different nodes. The proposed method first formulates the graph self-supervised learning problem as a multi-objective optimization problem, and then solves the problem with the preferred vector and personalized optimization. Experimental results demonstrate the effectiveness of the proposed method by achieving state-of-the-art performance.