Homophily Enhanced Graph Domain Adaptation

Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. In this paper, we highlight the significance of graph homophily, a pivotal factor for graph domain alignment, which, however, has long been overlooked in existing approaches. Specifically, our analysis first reveals that homophily discrepancies exist in benchmarks. Moreover, we also show that homophily discrepancies degrade GDA performance from both empirical and theoretical aspects, which further underscores the importance of homophily alignment in GDA. Inspired by this finding, we propose a novel homophily alignment algorithm that employs mixed filters to smooth graph signals, thereby effectively capturing and mitigating homophily discrepancies between graphs. Experimental results on a variety of benchmarks verify the effectiveness of our method.

Graphs are powerful ways to represent complex relationships, like how people interact on social networks or how information flows across the internet. In many real-world situations, useful information (like labels or categories) exists for one graph but not for another. Graph Domain Adaptation (GDA) helps transfer this knowledge from one graph to another, saving time and resources.In our research, we discovered that a key factor called homophily—the tendency for connected nodes to be similar is often different between graphs, and this mismatch can hurt GDA's performance. Surprisingly, this issue has largely been ignored until now.We studied how these differences affect results and found that aligning this similarity across graphs can make a big difference. We developed a new method to smooth out these differences and improve how well knowledge transfers between graphs. Our approach works well across various datasets, showing promise for improving learning from graph data in many applications, from recommendation systems to social networks.