Graph Models

Contextual Graph Markov Model

Introduced by Bacciu et al. in Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

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

Source: Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Density Estimation 1 50.00%
General Classification 1 50.00%

Components


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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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