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References: Label Propagation and Semi-Supervised Learning

  1. Label Propagation Algorithm - Wikipedia - Covers the family of iterative label propagation methods on graphs, including the Zhu & Ghahramani harmonic function formulation and the Zhou et al. label spreading variant, with discussion of convergence conditions.

  2. Belief Propagation - Wikipedia - Explains the sum-product and max-product message-passing algorithms on factor graphs, including exact inference on trees and the loopy BP approximation used when graphs contain cycles.

  3. SIR Model - Wikipedia - Describes the Susceptible-Infected-Recovered compartmental epidemic model, its differential equation formulation, the basic reproduction number R₀, and extensions to network-structured populations.

  4. Semi-Supervised Learning - Chapelle, O., Schölkopf, B., & Zien, A. (Eds.) - MIT Press - Comprehensive edited volume covering graph-based methods, manifold regularization, transductive SVMs, and generative approaches; Chapter 11 gives the definitive treatment of Gaussian fields and label propagation.

  5. Networks, Crowds, and Markets: Reasoning About a Highly Connected World - Easley, D. & Kleinberg, J. - Cambridge University Press - Rigorous undergraduate-accessible text unifying game theory, economics, and network science; Chapters 19–21 cover diffusion, cascades, and epidemic models with the same formalism used in this chapter.

  6. Semi-supervised Classification with Graph Convolutional Networks (Kipf & Welling, 2017) - arXiv - Introduces the GCN spectral convolution and frames node classification as a semi-supervised problem; the label propagation interpretation of GCN motivates the direct connection between this chapter and GNN foundations.

  7. Predict then Propagate: Graph Neural Networks meet Personalized PageRank (Gasteiger et al., 2019) - arXiv - Derives APPNP by replacing GCN aggregation with personalized PageRank diffusion, showing that separating feature transformation from label propagation improves both depth and accuracy.

  8. Combining Label Propagation and Simple Models Out-performs Graph Neural Networks (Huang et al., 2021) - arXiv - Introduces the Correct & Smooth (C&S) post-processing framework that applies two rounds of label diffusion on top of a plain MLP, achieving state-of-the-art results on ogbn-arxiv with minimal compute.

  9. Node Classification — PyTorch Geometric Documentation - PyTorch Geometric Docs - Step-by-step tutorial for semi-supervised node classification using GCN and related models on the Cora dataset, with runnable code that bridges the label propagation ideas in this chapter to practical GNN implementations.

  10. Papers With Code — Semi-Supervised Node Classification - Papers With Code - Tracks state-of-the-art results and links to reproducible implementations for semi-supervised node classification benchmarks including Cora, CiteSeer, and ogbn-arxiv, making it easy to compare label propagation baselines against current GNN methods.