Series
graph-theory-and-high-order-networks
5 parts, in order.
- 01
Jul 10, 2026
Revisiting the 2009 GNN Paper: Implementation (Part 2)
How the 2009 GNN model actually works: the state update and output functions, the two-phase learning algorithm, the training flow, and the linear vs nonlinear transition functions.
- 02
Jul 9, 2026
Revisiting the 2009 GNN Paper: Foundations (Part 1)
The theory behind Scarselli et al.'s original GNN paper: the tau function, information diffusion, universal approximation, and the positional/non-positional distinction.
- 03
Nov 6, 2025
What is a Hypergraph vs. a Regular (Relational) Graph?
Why hypergraphs are the promised representation for learning networks — higher-order relationships, spatial context, and why HyGNN beats plain GCN.
- 04
Sep 17, 2025
When Graphs Meet Medicine: The Computational Revolution in Drug Interaction Prediction
How graph and hypergraph neural networks turned drug interaction prediction from empirical guesswork into a computational discipline — and the cold-start problem still holding it back.
- 05
Feb 9, 2025
Graph Laplacian: From Basic Concepts to Modern Applications
How one matrix bridges discrete graphs and continuous math — Laplacian smoothing, spectral analysis, and why GNNs depend on it.