Year: 2017
Type: preprint
Abstract: Despite the wide use of networks as a versatile tool for exploring complex social systems, little is known about how to detect and forecast abrupt changes in social systems. In this report, we develop an early warning approach based on network properties to detect such changes. By analysing three collaborative social networks---one co-stardom, one patent and one scientific collaborative network, we discover that abrupt transitions inherent in these networks can serve as a good early warning signal, indicating, respectively, the dissolution of the Soviet Union, the emergence of the "soft matter" research field, and the merging of two scientific communities. We then develop a clique growth model that explains the universal properties of these real networks and find that they belong to a new universality class, described by the Gumbel distribution.
Source: arXiv (Cornell University)
Authors: Fan, Jingfang, Meng, Jun, Ding, Yimin, Du, Guangle, Li, Daqing, Cohen, Reuven, Chen, Xiaosong, Ye, Fangfu, Havlin, Shlomo
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Natural Science Foundation of China, Chinese Academy of Sciences, Council for Higher Education, Defense Threat Reduction Agency, Japan Science and Technology Agency, Planning and Budgeting Committee of the Council for Higher Education of Israel, Office of Naval Research
Awards: G6287949311, G938744503
OpenAlex ID: W2782220693