Graph self-supervised learning has gained increasing attention due to its
capacity to learn expressive node representations. Many pretext tasks, or loss
functions have been designed from distinct perspectives. However, we observe
that different pretext tasks affect downstream tasks differently cross
datasets, which suggests that searching pretext tasks is crucial for graph
self-supervised learning. Different from existing works focusing on designing
single pretext tasks, this work aims to investigate how to automatically
leverage multiple pretext tasks effectively. Nevertheless, evaluating
representations derived from multiple pretext tasks without direct access to
ground truth labels makes this problem challenging. To address this obstacle,
we make use of a key principle of many real-world graphs, i.e., homophily, or
the principle that "like attracts like," as the guidance to effectively search
various self-supervised pretext tasks. We provide theoretical understanding and
empirical evidence to justify the flexibility of homophily in this search task.
Then we propose the AutoSSL framework which can automatically search over
combinations of various self-supervised tasks. By evaluating the framework on 7
real-world datasets, our experimental results show that AutoSSL can
significantly boost the performance on downstream tasks including node
clustering and node classification compared with training under individual
tasks. Code is released at this https URL

Graph self-supervised learning can be improved by leveraging multiple pretext tasks through the AutoSSL framework, which uses homophily as guidance and significantly boosts the performance on downstream tasks.