Context-aided analysis of community evolution in networks

Pallotta, Giuliana; Konjevod, Goran; Cadena, Jose; Nguyen, Phan

doi:10.1002/sam.11354

Title: Context-aided analysis of community evolution in networks

Journal Article · Fri Sep 15 00:00:00 EDT 2017 · Statistical Analysis and Data Mining

DOI:https://doi.org/10.1002/sam.11354· OSTI ID:1393360

Pallotta, Giuliana ^[1];

^[1]; Cadena, Jose ^[2]; Nguyen, Phan ^[3]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Computational Engineering Division
Virginia Polytechnic Institute, Blacksburg, VA (United States). Department of Computer Science, Biocomplexity Institute
Northwestern Univ., Evanston, IL (United States). Department of Engineering Sciences and Applied Mathematics

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.

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Research Organization:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1393360

Report Number(s):: LLNL-JRNL-695017

Journal Information:: Statistical Analysis and Data Mining, Vol. 10, Issue 5; ISSN 1932-1864

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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