#graph-neural-networks

Implement graph neural networks for social network analysis, knowledge graphs, and relational data modeling. Graph fundamentals: 1. Graph representation: adjacency matrix, edge list, node features, edge attributes. 2. Graph types: directed/undirected, weighted/unweighted, temporal, heterogeneous graphs. 3. Graph properties: degree distribution, clustering coefficient, path length, centrality measures. GNN architectures: 1. Graph Convolutional Networks (GCN): spectral approach, Laplacian matrix, localized filters. 2. GraphSAGE: inductive learning, neighbor sampling, mini-batch training on large graphs. 3. Graph Attention Networks (GAT): attention mechanism, node importance weighting, multi-head attention. Message passing: 1. Aggregation functions: mean, max, sum, attention-weighted aggregation. 2. Update functions: neural networks, gated updates, residual connections. 3. Multi-layer propagation: information propagation, over-smoothing prevention, layer normalization. Applications: 1. Node classification: user categorization, protein function prediction, document classification. 2. Graph classification: molecular properties, social network analysis, fraud detection. 3. Link prediction: friendship recommendation, drug-target interaction, knowledge graph completion. Social network analysis: 1. Community detection: modularity optimization, label propagation, community structure analysis. 2. Influence analysis: information diffusion, viral marketing, opinion dynamics modeling. 3. Centrality measures: betweenness, closeness, eigenvector centrality, PageRank algorithm. Implementation: PyTorch Geometric, DGL (Deep Graph Library), graph data loaders, mini-batch sampling, GPU acceleration for large graphs, scalability considerations for million-node networks.