Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data

2019 ◽  
Vol 16 (5) ◽  
pp. 1743-1752
Author(s):  
Yishu Wang ◽  
Huaying Fang ◽  
Dejie Yang ◽  
Hongyu Zhao ◽  
Minghua Deng
Keyword(s):  
Random Graph ◽  
Clustering Analysis ◽  
Exponential Family ◽  
Genetic Interaction ◽  
Network Clustering ◽  
Interaction Data ◽  
Graph Models ◽  
Random Graph Models ◽  
Genetic Interaction Data

scholarly journals Exponential-Family Random Graph Models for Multi-Layer Networks

Psychometrika ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. 630-659
Author(s):  
Pavel N. Krivitsky ◽  
Laura M. Koehly ◽  
Christopher Steven Marcum
Keyword(s):  
Random Graph ◽  
Exponential Family ◽  
Graph Models ◽  
Random Graph Models

scholarly journals Exponential-Family Random Graph Models for Multi-Layer Networks

2019 ◽  
Author(s):  
Pavel N Krivitsky ◽  
Laura Koehly ◽  
Christopher Steven Marcum
Keyword(s):  
Random Graph ◽  
Binomial Distribution ◽  
Exponential Family ◽  
Published Data ◽  
Data Sets ◽  
Graph Models ◽  
Random Graph Models ◽  
Special Cases

Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway--Maxwell-Binomial distribution to model the marginal dependence among multiple layers; a "layer logic" language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and non-degenerate triadic and degree effects. The developments are demonstrated on two previously published data sets.

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