Summary
In this chapter, we explored a new task with link prediction. We gave an overview of this field by presenting heuristic and matrix factorization techniques. Heuristics can be classified according to the k-hop neighbors they consider – from local with 1-hop neighbors to global with the knowledge of the entire graph. Conversely, matrix factorization approximates the adjacency matrix using node embeddings. We also explained how this technique was connected to algorithms described in previous chapters (DeepWalk and Node2Vec).
After this introduction to link prediction, we saw how to implement it using GNNs. We outlined two kinds of techniques, based on node embeddings (GAE and VGAE) and subgraph representations (SEAL). Finally, we implemented a VGAE and SEAL on the Cora dataset with an edge-level random split and negative sampling. Both models obtained comparable performance, although SEAL is strictly more expressive.
In Chapter 11, Generating Graphs with Graph Neural...
