  Recent research has demonstrated the efficacy of pre-training graph neural
networks (GNNs) to capture the transferable graph semantics and enhance the
performance of various downstream tasks. However, the semantic knowledge
learned from pretext tasks might be unrelated to the downstream task, leading
to a semantic gap that limits the application of graph pre-training. To reduce
this gap, traditional approaches propose hybrid pre-training to combine various
pretext tasks together in a multi-task learning fashion and learn multi-grained
knowledge, which, however, cannot distinguish tasks and results in some
transferable task-specific knowledge distortion by each other. Moreover, most
GNNs cannot distinguish nodes located in different parts of the graph, making
them fail to learn position-specific knowledge and lead to suboptimal
performance. In this work, inspired by the prompt-based tuning in natural
language processing, we propose a unified framework for graph hybrid
pre-training which injects the task identification and position identification
into GNNs through a prompt mechanism, namely multi-task graph dual prompt
(ULTRA-DP). Based on this framework, we propose a prompt-based transferability
test to find the most relevant pretext task in order to reduce the semantic
gap. To implement the hybrid pre-training tasks, beyond the classical edge
prediction task (node-node level), we further propose a novel pre-training
paradigm based on a group of $k$-nearest neighbors (node-group level). The
combination of them across different scales is able to comprehensively express
more structural semantics and derive richer multi-grained knowledge. Extensive
experiments show that our proposed ULTRA-DP can significantly enhance the
performance of hybrid pre-training methods and show the generalizability to
other pre-training tasks and backbone architectures.
