Social distance and network structures

Social distance and network structures

Dados Bibliográficos

ID7516290
AutoresRyota Iijima (0009-0007-5466-6915, Yale University, autor correspondente), Yuichiro Kamada (0000-0002-2976-9038, University of California, Berkeley, autor correspondente)
Ano2017
Volume12
Fascículo2
Páginas655-689
Data de publicação2017-05-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoTheoretical Economics (JOURNAL)
Identificadores do periódicoISSN: 1555-7561 • E-ISSN: 1933-6837
EditoraEconometric Society (PUBLISHER • US)
DOI10.3982/te1873
OpenAlexW2495036290
IdiomaEN
Citações recebidas5
Referências citadas49

This paper proposes a tractable model that allows us to analyze how agents' perception of relationships with others determines the structures of networks. In our model, agents are endowed with their own multidimensional characteristics and their payoffs depend on the social distance between them. We characterize the clustering coefficient and average path length in stable networks, and analyze how they are related to the way agents measure social distances. The model predicts the small-world properties under a class of social distance that violates the triangle inequality. Allowing for heterogeneity in link-formation costs, the model also accommodates other well documented empirical patterns of social networks such as skewed degree distributions, positive assortativity of degrees, and clustering-degree correlation

Assortativity · Average path length · Cluster analysis · Clustering coefficient · Combinatorics · Complex network · Data mining · Degree (music · Degree distribution · Econometrics · Mathematical economics · Measure (data warehouse · Path (computing · Shortest path problem · Social network (sociolinguistics · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Media and Politics · Artificial Intelligence · Theoretical Computer Science