A Network Formation Model Based on Subgraphs

• 成果类型: Article; Early Access
• 署名作者: Chandrasekhar, Arun G.; Jackson, Matthew O.
• 署名单位: National Bureau of Economic Research; Stanford University; Stanford University
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdaf013
• 发表日期: 2025
• 关键词: covariance-matrix estimation; social networks; normal approximations; graphs; distributions; DYNAMICS; CHOICE; WORLD; BIAS

摘要

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.