  Link prediction is a fundamental problem in graph data analysis. While most
of the literature focuses on transductive link prediction that requires all the
graph nodes and majority of links in training, inductive link prediction, which
only uses a proportion of the nodes and their links in training, is a more
challenging problem in various real-world applications. In this paper, we
propose a meta-learning approach with graph neural networks for link
prediction: Neural Processes for Graph Neural Networks (NPGNN), which can
perform both transductive and inductive learning tasks and adapt to patterns in
a large new graph after training with a small subgraph. Experiments on
real-world graphs are conducted to validate our model, where the results
suggest that the proposed method achieves stronger performance compared to
other state-of-the-art models, and meanwhile generalizes well when training on
a small subgraph.
