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Link prediction is a fundamental problem in graph data analysis. While mostof the literature focuses on transductive link prediction that requires all thegraph nodes and majority of links in training, inductive link prediction, whichonly uses a proportion of the nodes and their links in training, is a morechallenging problem in various real-world applications. In this paper, wepropose a meta-learning approach with graph neural networks for linkprediction: Neural Processes for Graph Neural Networks (NPGNN), which canperform both transductive and inductive learning tasks and adapt to patterns ina large new graph after training with a small subgraph. Experiments onreal-world graphs are conducted to validate our model, where the resultssuggest that the proposed method achieves stronger performance compared toother state-of-the-art models, and meanwhile generalizes well when training ona small subgraph.

How Neural Processes Improve Graph Link Prediction