Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
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Abstract (arXiv)
We introduce the Contextual Graph Markov Model, 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.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 May 2018.
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Citing papers per year: 2021: 7; 2022: 3; 2023: 3.
13 papers.
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- 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
- 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
- 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreachingcited by 24
- 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactionscited by 21
- 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
- 2021GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generationcited by 10
- 2021ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecturecited by 9
- 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Surveycited by 8
- 2023Modeling Edge Features with Deep Bayesian Graph Networkscited by 1
- 2021Reinforced Contact Tracing and Epidemic Interventioncited by 0
- 2021Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positionscited by 0
- 2021Capsule Graph Neural Networks with EM Routingcited by 0
1 more
- 2022Bayesian Deep Learning for Graphscited by 0
- 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching27 Dec 2023
- 2023Modeling Edge Features with Deep Bayesian Graph Networks17 Aug 2023
- 2023A Survey on Malware Detection with Graph Representation Learning28 Mar 2023
- 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges21 Oct 2022
- 2022Bayesian Deep Learning for Graphs24 Feb 2022
- 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey15 Feb 2022
- 2021Capsule Graph Neural Networks with EM Routing18 Oct 2021
- 2021GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation18 Jul 2021
- 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs3 Jul 2021
- 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions14 Jun 2021
- 2021Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions24 May 2021
- 2021ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture24 Mar 2021
1 more
- 2021Reinforced Contact Tracing and Epidemic Intervention4 Feb 2021
- 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
- 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challengescited by 30
- 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreachingcited by 24
- 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactionscited by 21
- 2023A Survey on Malware Detection with Graph Representation Learningcited by 11
- 2021GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generationcited by 10
- 2021ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecturecited by 9
- 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Surveycited by 8
- 2023Modeling Edge Features with Deep Bayesian Graph Networkscited by 1
- 2021Reinforced Contact Tracing and Epidemic Interventioncited by 0
- 2021Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positionscited by 0
- 2021Capsule Graph Neural Networks with EM Routingcited by 0
12 most cited of 13 All 13 (JSON)
- 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching27 Dec 2023
- 2023Modeling Edge Features with Deep Bayesian Graph Networks17 Aug 2023
- 2023A Survey on Malware Detection with Graph Representation Learning28 Mar 2023
- 2022A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges21 Oct 2022
- 2022Bayesian Deep Learning for Graphs24 Feb 2022
- 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey15 Feb 2022
- 2021Capsule Graph Neural Networks with EM Routing18 Oct 2021
- 2021GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation18 Jul 2021
- 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs3 Jul 2021
- 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions14 Jun 2021
- 2021Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions24 May 2021
- 2021ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture24 Mar 2021
12 newest of 13 All 13 (JSON)