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

• DOI: 10.48550/arxiv.1805.10636
• Publication Date: 2018-01-01
• Authors: Davide Bacciu; Federico Errica; Alessio Micheli
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Computer Science - Neural and Evolutionary Computing; Machine Learning (stat.ML); 02 engineering and technology; Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics - Machine Learning; 0202 electrical engineering, electronic engineering, information engineering; Neural and Evolutionary Computing (cs.NE); Computational Theory and Mathematics

We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for processing of graph data. It founds on a constructive methodology to build deep architecture comprising layers probabilistic that learn encode structured information in incremental fashion. Context is diffused efficient scalable way across vertexes edges. The resulting encoding used combination with discriminative address structure classification benchmarks.