Exploring Correlations of Self-supervised Tasks for Graphs

arXiv:2405.04245v1 Announce Type: new
Abstract: Graph self-supervised learning has sparked a research surge in training informative representations without accessing any labeled data. However, our understanding of graph self-supervised learning remains limited, and the inherent relationships between various self-supervised tasks are still unexplored. Our paper aims to provide a fresh understanding of graph self-supervised learning based on task correlations. Specifically, we evaluate the performance of the representations trained by one specific task on other tasks and define correlation values to quantify …

abstract arxiv correlations cs.ai cs.lg data graph graphs however paper relationships research self-supervised learning supervised learning tasks training type understanding