Methods › Graphs › Graph Models › CGMM
Contextual Graph Markov Model
CGMM
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Contextual Graph Markov Model (CGMM) is an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.
Description and image from: Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Cancer-inspired Genomics Mapper Model for the Generation of Synthetic DNA Sequences with Desired Genomics Signatures 1 May 2023 · 0 repositories · arXiv:2305.01475
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A new perspective on probabilistic image modeling 21 Mar 2022 · 0 repositories · arXiv:2203.11034
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Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing 27 May 2018 · 1 repository · arXiv:1805.10636
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Density Estimation | 1 |
| General Classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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