Research map / Graph learning / Graph algorithms and clustering
The Linearization of Belief Propagation on Pairwise Markov Random Fields
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace loopy BP with linearized BP (matrix formulation) | pairwise MRF node labeling · synthetic planted-potential graphs | inference speed per iteration: 50 times faster per iteration | Helped |
| replace loopy BP with linearized BP, weak potentials | pairwise MRF node labeling · synthetic planted-potential graphs | labeling accuracy | No clear effect |
| replace loopy BP with linearized BP, strong potentials (h > 3) | pairwise MRF node labeling · synthetic planted-potential graphs | labeling accuracy | Hurt |
| stop iterating early with convergence parameter s near or above 1 (ablation) | linearized BP · synthetic graph, f=0.05, h=8, d=5 | labeling accuracy | Helped |
Closest papers by meaning
- Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs (ICML 2026 · similarity 0.90)
- Block Belief Propagation for Parameter Learning in Markov Random Fields (AAAI 2019 · similarity 0.89)
- Synthesis of MCMC and Belief Propagation (NeurIPS 2016 · similarity 0.89)
- Beyond Trees: Analysis and Convergence of Belief Propagation in Graphs with Multiple Cycles (AAAI 2020 · similarity 0.89)
- Inference in Graphical Models via Semidefinite Programming Hierarchies (NeurIPS 2017 · similarity 0.88)
- Tensor Belief Propagation (ICML 2017 · similarity 0.88)
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