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Link prediction is central to many real-world applications, but itsperformance may be hampered when the graph of interest is sparse. To alleviateissues caused by sparsity, we investigate a previously overlooked phenomenon:in many cases, a densely connected, complementary graph can be found for theoriginal graph. The denser graph may share nodes with the original graph, whichoffers a natural bridge for transferring selective, meaningful knowledge. Weidentify this setting as Graph Intersection-induced Transfer Learning (GITL),which is motivated by practical applications in e-commerce or academicco-authorship predictions. We develop a framework to effectively leverage thestructural prior in this setting. We first create an intersection subgraphusing the shared nodes between the two graphs, then transfer knowledge from thesource-enriched intersection subgraph to the full target graph. In the secondstep, we consider two approaches: a modified label propagation, and amulti-layer perceptron (MLP) model in a teacher-student regime. Experimentalresults on proprietary e-commerce datasets and open-source citation graphs showthat the proposed workflow outperforms existing transfer learning baselinesthat do not explicitly utilize the intersection structure.

You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction