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Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization by Bryan Perozzi; Leman Akoglu is a Computer Science article available to read on EtoBox.

What is Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization about?

Given a network with node attributes, how can we identify communities and spot anomalies? How can we characterize, describe, or summarize the network in a succinct way? Community extraction requires a measure of quality for connected subgraphs (e.g., social circles). Existing subgraph measures, however, either consider only the connectedness of nodes inside the community and ignore the cross-edges at the boundary (e.g., density) or only quantify the structure of the community and ignore the node attributes (e.g., conductance). In this work, we focus on node-attributed networks and introduce: (1) a __new measure of subgraph quality__ for attributed communities called normality, (2) a __community extraction__ algorithm that uses normality to extract communities and a few characterizing attributes per community, and (3) a __summarization and interactive visualization__ approach for attributed graph exploration. More specifically, (1) we first introduce a new measure to quantify the normality of an attributed subgraph. Our normality measure carefully utilizes structure and attributes together to quantify both the internal consistency and external separability. We then formulate an obje

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Author

Bryan Perozzi; Leman Akoglu

Publisher

ACM

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)