Articles | Network Graph Analysis

Network Graph Analysis

A nine-part series on graph representations, community structure, knowledge graphs, and graph deep learning.

• Part 1: FoundationsGraph representations, key metrics, and visualization with NetworkX
• Part 2: Community Detection in Social GraphsModularity, Louvain, and stochastic block models for finding structure
• Part 3: Centrality, Influence, and Spectral MethodsWho matters in a network: from degree counts to the graph Laplacian
• Part 4: Building Knowledge Graphs from TextFrom raw text to structured concept networks
• Part 5: LLM-Augmented Knowledge GraphsUsing large language models to extract structured knowledge and build navigable graphs
• Part 6: GraphRAG: Retrieval-Augmented Generation over GraphsGraph-based retrieval for grounding LLM generation in structured, relational context
• Part 7: Graph Neural Networks: From Spectral to SpatialDeep learning on graph-structured data with GCN, GAT, and message passing
• Part 8: Temporal Graph NetworksModeling graphs that evolve over time
• Part 9: Causal and Bayesian Graph NetworksDAGs, interventions, and counterfactual reasoning on graphs