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A Network Formation Model Based on Subgraphs
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AI
DOI：10.1093/restud/rdaf013.png)
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Abstract
En 中文
We develop a new class of random graph models for the statistical estimation of network formation-subgraph generated models (SUGMs). Various subgraphs-e.g. links, triangles, cliques, stars-are generated and their union results in a network. We show that SUGMs are identified and establish the consistency and asymptotic distribution of parameter estimators in empirically relevant cases. We show that a simple four-parameter SUGM matches basic patterns in empirical networks more closely than four standard models (with many more dimensions): (1) stochastic block models; (2) models with node-level unobserved heterogeneity; (3) latent space models; and (4) exponential random graphs. We illustrate the framework's value via several applications using networks from rural India. We study whether network structure helps enforce risk-sharing and whether cross-caste interactions are more likely to be private. We also develop a new central limit theorem for correlated random variables, which is required to prove our results and is of independent interest.
Keywords:
Subgraphs
Random networks
Random graphs
Exponential random graph models
Exponential family
Social networks
Network formation
Consistency
Central limit theorem
Sparse networks
Multiplex
Multigraphs