Articles

Writing on networks, experiments, and computation

Decision Science

• Part 1: Online Experiments with a Bayesian LensFrom conversion metrics to posterior intuition
• Part 2: How the Bayesian Approach Improves Sample EfficiencyPrior knowledge, adaptive designs, and why you need fewer participants
• Part 3: The Secretary Problem and Optimal StoppingWhen to stop looking and commit: the 37% rule and beyond
• Part 4: Multi-Armed Bandits and Thompson SamplingFrom fixed-horizon tests to adaptive allocation and regret minimization
• Part 5: Causal Inference from Observational DataPropensity scores, difference-in-differences, and what randomization buys you
• Part 6: Sequential Testing and Early StoppingMonitoring experiments in real time without inflating error rates

Network Graph Analysis

• 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

Standalone

• Article Press DemoJSON → Static HTML with code execution
• Fidelity in LLM Information ProcessingPreserving source signal through summarization, compression, and context engineering