Machine Learning on Graphs, from Graph Topology to Applocations

Annuolina Roxta

Abstract

Machine Learning on Graphs, from Graph Topology to Applocations

Contemporary data analytics applications on graphs frequently operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, somewhat than serving as prior knowledge which aids the problematic solution. Part III of this monograph starts by a comprehensive account of ways to study the pertinent graph topology, reaching from the humblest case where the physics of the problem previously suggest a likely graph structure, through to general cases where the graph structure is to be learned from the data experiential on a graph.