Tackling Negative Transfer on Graphs

Transfer learning aims to boost the learning on the target task leveraging knowledge learned from other relevant tasks. However, when the source and target are not closely related, the learning performance may be adversely affected, a phenomenon known as negative transfer. In this paper, we investigate the negative transfer in graph transfer learning, which is important yet underexplored. We reveal that, unlike image or text, negative transfer commonly occurs in graph-structured data, even when source and target graphs share semantic similarities. Specifically, we identify that structural differences significantly amplify the dissimilarities in the node embeddings across graphs. To mitigate this, we bring a new insight: for semantically similar graphs, although structural differences lead to significant distribution shift in node embeddings, their impact on subgraph embeddings could be marginal. Building on this insight, we introduce two effective yet elegant methods, Subgraph Pooling (SP) and Subgraph Pooling++ (SP++), that transfer subgraph-level knowledge across graphs. We theoretically analyze the role of SP in reducing graph discrepancy and conduct extensive experiments to evaluate its superiority under various settings. Our code and datasets are available at: https://github.com/Zehong-Wang/Subgraph-Pooling.