Multiresolution Network Models.

Clicks: 237
ID: 17504
2019
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Ranked #43 of 100 articles by views in journal of computational and graphical statistics : a joint publication of american statistical association, institute of mathematical statistics, interface foundation of north america

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Abstract
Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of the graph, whereas projection-based approaches (e.g., the latent space model in the statistics literature) represent in rich detail the roles of individuals. Many pertinent questions in sociology and economics, however, span multiple scales of analysis. Further, many questions involve comparisons across disconnected graphs that will, inevitably be of different sizes, either due to missing data or the inherent heterogeneity in real-world networks. We propose a class of network models that represent network structure on multiple scales and facilitate comparison across graphs with different numbers of individuals. These models differentially invest modeling effort within subgraphs of high density, often termed communities, while maintaining a parsimonious structure between said subgraphs. We show that our model class is projective, highlighting an ongoing discussion in the social network modeling literature on the dependence of inference paradigms on the size of the observed graph. We illustrate the utility of our method using data on household relations from Karnataka, India. Supplementary material for this article is available online.
Reference Key
fosdick2019multiresolutionjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fosdick, Bailey K;McCormick, Tyler H;Murphy, Thomas Brendan;Ng, Tin Lok James;Westling, Ted;
Journal journal of computational and graphical statistics : a joint publication of american statistical association, institute of mathematical statistics, interface foundation of north america
Year 2019
DOI
10.1080/10618600.2018.1505633
URL
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