603 papers with code • 73 benchmarks • 56 datasets
Link prediction is a task to estimate the probability of links between nodes in a graph.
( Image credit: Inductive Representation Learning on Large Graphs )
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.