Analyzing Network Formation Using Big Data Analytics: A Study of Degree Distribution, Clustering Coefficient, Diameter, and Assortativity

• 成果类型: Article; Early Access
• 署名作者: Li, Hao; Xu, Tianzhuang; Qi, Jin; Chen, Ying-Ju
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.00475
• 发表日期: 2026
• 关键词: Network formation; random graph; edge-based search process; network data

摘要

This research presents an innovative approach to network development which goes beyond conventional models that use connection rules for individual agents. We show that agents can produce diverse network structures through uniform edge searches which involve random connection attempts without preference. This model offers a new viewpoint on network formation by emphasizing edges in contrast to nodes. Within this framework, edges are considerably more crucial than those in other network formation models as they represent tangible interactions such as question-and-answer exchanges, commercial transactions, and coauthorships, rather than abstract relationships. We prove that the model converges to a stationary equilibrium degree distribution, implying that in the long term, the probability of observing certain network characteristics stabilizes and becomes time-invariant. Furthermore, we derive closed-form asymptotic expressions for key network statistics and validate them through both simulations and real-world data sets. Our research shows that three core dimensions are inherently in competition with one another: (1) quantity (mean degree), reflecting agents' overall connectivity; (2) quality (clustering coefficient), capturing local cohesion; and (3) equity (skewness of the in-degree distribution), indicating how strongly connections concentrate on a few nodes. Our results show that raising the search times boosts quantity but lowers quality by diluting clustering, whereas raising the secondary connection probability enhances clustering yet decreases equity by concentrating in-degree and intensifying the Matthew effect. These trade-offs reveal essential limits of network formation while providing a flexible framework which connects theoretical insight to practical decisions in network design and management.