ICLR 2025

Published as a conference paper at ICLR 2025
GRAPHBRIDGE:
TOWARDS ARBITRARY TRANSFER
LEARNING IN GNNS
Li Ju, Xingyi Yang, Qi Li, Xinchao Wang∗
National University of Singapore
{jujulili,xyang,liqi}@u.nus.edu, xinchao@nus.edu.sg
ABSTRACT
Graph neural networks (GNNs) are conventionally trained on a per-domain, per-
task basis. It creates a significant barrier in transferring the acquired knowledge
to different, heterogeneous data setups. This paper introduces GraphBridge, a
novel framework to enable knowledge transfer across disparate tasks and domains
in GNNs, circumventing the need for modifications to task configurations or graph
structures. Specifically, GraphBridge allows for the augmentation of any pre-
trained GNN with prediction heads and a bridging network that connects the input
to the output layer. This architecture not only preserves the intrinsic knowledge of
the original model but also supports outputs of arbitrary dimensions. To mitigate
the negative transfer problem, GraphBridge merges the source model with a con-
currently trained model, thereby reducing the source bias when applied to the tar-
get domain. Our method is thoroughly evaluated across diverse transfer learning
scenarios, including Graph2Graph, Node2Node, Graph2Node, and graph2point-
cloud. Empirical validation, conducted over 16 datasets representative of these
scenarios, confirms the framework’s capacity for task- and domain-agnostic trans-
fer learning within graph-like data, marking a significant advancement in the field
of GNNs. Code is available at https://github.com/jujulili888/GraphBridge.
1
INTRODUCTION
With the explosive growth of graph data, the application of Graph Neural Networks (GNNs) has
become increasingly widespread in domains (Jing et al., 2022; 2021b; Wu et al., 2020b; Yu et al.,
2018; Shah et al., 2020) such as recommendation systems (Gao et al., 2022; Wu et al., 2019) and
biopharmaceutics(Zitnik et al., 2018; Rathi et al., 2019). Despite their growing popularity, the ...