Multiresolution network models

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Title: Multiresolution network models
Authors: Fosdick, Bailey K.
McCormick, Tyler H.
Murphy, Thomas Brendan
Ng, Tin Lok James
Westling, Ted
Permanent link: http://hdl.handle.net/10197/10620
Date: 5-Nov-2018
Online since: 2019-05-22T13:54:15Z
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Taylor & Francis
Journal: Journal of Computational and Graphical Statistics
Volume: 28
Issue: 1
Copyright (published version): 2018 Taylor & Francis Group
Keywords: Latent spaceMultiscaleProjectivitySocial networkStochastic blockmodel
DOI: 10.1080/10618600.2018.1505633
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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