Inductive Graph Alignment Prompt: Bridging the Gap between Graph
  Pre-training and Inductive Fine-tuning From Spectral Perspective
Yuchen Yan Peiyan Zhang Zheng Fang Qingqing Long
The "Graph pre-training and fine-tuning" paradigm has significantly improved
Graph Neural Networks(GNNs) by capturing general knowledge without manual
annotations for downstream tasks. However, due to the immense gap of data and
tasks between the pre-training and fine-tuning stages, the model performance is
still limited. Inspired by prompt fine-tuning in Natural Language
Processing(NLP), many endeavors have been made to bridge the gap in graph
domain. But existing methods simply reformulate the form of fine-tuning tasks
to the pre-training ones. With the premise that the pre-training graphs are
compatible with the fine-tuning ones, these methods typically operate in
transductive setting. In order to generalize graph pre-training to inductive
scenario where the fine-tuning graphs might significantly differ from
pre-training ones, we propose a novel graph prompt based method called
Inductive Graph Alignment Prompt(IGAP). Firstly, we unify the mainstream graph
pre-training frameworks and analyze the essence of graph pre-training from
graph spectral theory. Then we identify the two sources of the data gap in
inductive setting: (i) graph signal gap and (ii) graph structure gap. Based on
the insight of graph pre-training, we propose to bridge the graph signal gap
and the graph structure gap with learnable prompts in the spectral space. A
theoretical analysis ensures the effectiveness of our method. At last, we
conduct extensive experiments among nodes classification and graph
classification tasks under the transductive, semi-inductive and inductive
settings. The results demonstrate that our proposed method can successfully
bridge the data gap under different settings.