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. 

Author(s): Annuolina Roxta

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