Recent years have witnessed the great success of graph pre-training for graph
representation learning. With hundreds of graph pre-training tasks proposed,
integrating knowledge acquired from multiple pre-training tasks has become a
popular research topic. In this paper, we identify two important collaborative
processes for this topic: (1) select: how to select an optimal task combination
from a given task pool based on their compatibility, and (2) weigh: how to
weigh the selected tasks based on their importance. While there currently has
been a lot of work focused on weighing, comparatively little effort has been
devoted to selecting. This paper proposes a novel instance-level framework for
integrating multiple graph pre-training tasks, Weigh And Select (WAS), where
the two collaborative processes, weighing and selecting, are combined by
decoupled siamese networks. Specifically, it first adaptively learns an optimal
combination of tasks for each instance from a given task pool, based on which a
customized instance-level task weighing strategy is learned. Extensive
experiments on 16 graph datasets across node-level and graph-level downstream
tasks have demonstrated that by combining a few simple but classical tasks, WAS
can achieve comparable performance to other leading counterparts. The code is
available at this https URL

本文提出了一个新颖的实例级框架，Weigh And Select (WAS)，通过分离连接的孪生网络将合作过程，包括选择和加权，结合起来，从而实现多个图先训练任务的整合。通过将一些简单但经典的任务相结合，WAS 在节点级和图级后续任务上表现出与其他主流方法相当的性能。