9 docs tagged with "graph-neural-networks"

How LightGCN, PinSage, and NGCF use graph neural networks on user-item interaction graphs to capture multi-hop collaborative filtering signals at billion-scale.

GAT - learning which neighbors matter via attention over graph edges. Multi-head attention, GATv2's dynamic attention, heterophilic graphs, and training on Cora with PyTorch Geometric.

GCN derivation from spectral graph theory to efficient spatial message passing. Symmetric normalization, renormalization trick, over-smoothing, and training on Cora with PyG.

Node embeddings from shallow methods to GNNs - DeepWalk, Node2Vec, LINE, spectral embeddings, manual features, and their fundamental limitations. How to featurize nodes, edges, and graphs.

GraphSAGE - sample and aggregate for inductive GNNs that generalize to unseen nodes. Neighbor sampling, mini-batch training, unsupervised learning, and PinSage for billion-scale recommendations.

TransE, RotatE, CompGCN - embedding entities and relations in vector spaces to predict missing facts in knowledge graphs, enabling AI systems to reason about structured world knowledge.

MPNN - the unified framework showing GCN, GraphSAGE, and GAT are special cases of a single message-passing paradigm with a fundamental 1-WL expressivity ceiling.

Master graph neural networks for drug discovery, fraud detection, and recommendations. GCN, GAT, GraphSAGE, MPNN, and knowledge graph embeddings with PyTorch Geometric.

When tabular data fails - graph formalism, adjacency matrix, Laplacian, graph types, real-world datasets, the Weisfeiler-Lehman test, and why CNNs cannot handle graph-structured data.