  Recently, the pretrain-finetuning paradigm has attracted tons of attention in
graph learning community due to its power of alleviating the lack of labels
problem in many real-world applications. Current studies use existing
techniques, such as weight constraint, representation constraint, which are
derived from images or text data, to transfer the invariant knowledge from the
pre-train stage to fine-tuning stage. However, these methods failed to preserve
invariances from graph structure and Graph Neural Network (GNN) style models.
In this paper, we present a novel optimal transport-based fine-tuning framework
called GTOT-Tuning, namely, Graph Topology induced Optimal Transport
fine-Tuning, for GNN style backbones. GTOT-Tuning is required to utilize the
property of graph data to enhance the preservation of representation produced
by fine-tuned networks. Toward this goal, we formulate graph local knowledge
transfer as an Optimal Transport (OT) problem with a structural prior and
construct the GTOT regularizer to constrain the fine-tuned model behaviors. By
using the adjacency relationship amongst nodes, the GTOT regularizer achieves
node-level optimal transport procedures and reduces redundant transport
procedures, resulting in efficient knowledge transfer from the pre-trained
models. We evaluate GTOT-Tuning on eight downstream tasks with various GNN
backbones and demonstrate that it achieves state-of-the-art fine-tuning
performance for GNNs.
