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Graph Neural Networks Part 4: Teaching Models to Connect the Dots
- Link prediction is the task of forecasting missing or future connections between nodes in a graph.
- Local heuristics like Common Neighbors, Jaccard Coefficient, and Adamic-Adar Index are based on the immediate neighborhood of nodes.
- Preferential Attachment is a different approach where nodes with higher degrees are more likely to form links.
- Global heuristics like Katz Index and Rooted PageRank consider paths or the entire graph structure for link prediction.
- Machine learning approaches for link prediction treat it as a binary classification task and can outperform heuristics.
- VGAE combines Graph Auto-Encoder (GAE) and Variational Auto-Encoder (VAE) to predict missing links based on learned hidden structure.
- VGAE's encoding step maps nodes to latent variables in a hidden space, while the decoding step predicts edges between nodes.
- SEAL (Subgraph Embedding-based Link Prediction) looks at local subgraphs instead of global node embeddings for predicting links.
- SEAL learns structural patterns directly from examples, providing better performance for some problems.
- VGAE and SEAL are powerful GNN-based methods that offer improved link prediction results compared to traditional heuristics and basic machine learning models.
- Understanding link prediction methodologies, from simple heuristics to advanced GNN-based models like VGAE and SEAL, can enhance predictions in a variety of applications.
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