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Title: A local structure model for network analysis

The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending on neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.
Authors:
 [1] ;  [2] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Iowa State Univ., Ames, IA (United States). Dept. of Statistics
Publication Date:
Report Number(s):
LA-UR-16-20622
Journal ID: ISSN 1938-7989
Grant/Contract Number:
AC52-06NA25396; DMS-1406747
Type:
Accepted Manuscript
Journal Name:
Statistics and Its Interface
Additional Journal Information:
Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 1938-7989
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE; SNL Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Mathematics
OSTI Identifier:
1409754

Casleton, Emily, Nordman, Daniel, and Kaiser, Mark. A local structure model for network analysis. United States: N. p., Web. doi:10.4310/SII.2017.v10.n2.a15.
Casleton, Emily, Nordman, Daniel, & Kaiser, Mark. A local structure model for network analysis. United States. doi:10.4310/SII.2017.v10.n2.a15.
Casleton, Emily, Nordman, Daniel, and Kaiser, Mark. 2017. "A local structure model for network analysis". United States. doi:10.4310/SII.2017.v10.n2.a15. https://www.osti.gov/servlets/purl/1409754.
@article{osti_1409754,
title = {A local structure model for network analysis},
author = {Casleton, Emily and Nordman, Daniel and Kaiser, Mark},
abstractNote = {The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending on neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.},
doi = {10.4310/SII.2017.v10.n2.a15},
journal = {Statistics and Its Interface},
number = 2,
volume = 10,
place = {United States},
year = {2017},
month = {4}
}