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The Internet has provided us with a number of online collaborative environments, including platforms for open software developments and online encyclopedias such as Wikipedia. Conflicts may arise in the course of such collaboration, but despite differences of opinion consensus can be reached. By investigating the consensus-building processes, we can shed light on the dynamics of social behavior. In Wikipedia, it is not always easy for editors to agree about article content, especially considering people's different tolerance levels towards others and for whatever may be written. In this paper, we focus on how the editors' attitudes, namely being broad-minded or stubborn, affect the consensus-building process in a model of Wikipedia. We further investigate how banning editors affects the speed with which conflicts or debates can be resolved. For the analysis, we use an agent-based opinion model developed to simulate different aspects of Wikipedia. We show that, in most cases, banning agents from editing an article slows down the consensus-building process, and increases the system's relaxation time. We show further, and counterintuitively, that with large groups of "extremists" who hold other than the central opinion, consensus can be reached faster and the article will be less biased.

Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down rapidly. Using social network datasets we show the main factors leading to such a dramatic collapse. At early stage mostly the loosely bound users disappear, later collective effects play the main role leading to cascading failures. We present a theory based on a generalised threshold model to explain the findings and show how the collapse time can be estimated in advance using the dynamics of the churning users. Our results shed light to possible mechanisms of instabilities in other competing social processes.

Interactions between humans give rise to complex social networks that are characterized by heterogeneous degree distribution, weight-topology relation, overlapping community structure, and dynamics of links. Understanding these characteristics of social networks is the primary goal of their research as they constitute scaffolds for various emergent social phenomena from disease spreading to political movements. An appropriate tool for studying them is agent-based modeling, in which nodes, representing individuals, make decisions about creating and deleting links, thus yielding various macroscopic behavioral patterns. Here we focus on studying a generalization of the weighted social network model, being one of the most fundamental agent-based models for describing the formation of social ties and social networks. This generalized weighted social network (GWSN) model incorporates triadic closure, homophilic interactions, and various link termination mechanisms, which have been studied separately in the previous works. Accordingly, the GWSN model has an increased number of input parameters and the model behavior gets excessively complex, making it challenging to clarify the model behavior. We have executed massive simulations with a supercomputer and used the results as the training data for deep neural networks to conduct regression analysis for predicting the properties of the generated networks from the input parameters. The obtained regression model was also used for global sensitivity analysis to identify which parameters are influential or insignificant. We believe that this methodology is applicable for a large class of complex network models, thus opening the way for more realistic quantitative agent-based modeling.

Funding Information: YM, H-HJ, JT, and JK are thankful for the hospitality of Aalto University. This research used computational resources of the supercomputer Fugaku provided by the RIKEN Center for Computational Science. Publisher Copyright: © Copyright © 2021 Murase, Jo, Török, Kertész and Kaski. ; Interactions between humans give rise to complex social networks that are characterized by heterogeneous degree distribution, weight-topology relation, overlapping community structure, and dynamics of links. Understanding these characteristics of social networks is the primary goal of their research as they constitute scaffolds for various emergent social phenomena from disease spreading to political movements. An appropriate tool for studying them is agent-based modeling, in which nodes, representing individuals, make decisions about creating and deleting links, thus yielding various macroscopic behavioral patterns. Here we focus on studying a generalization of the weighted social network model, being one of the most fundamental agent-based models for describing the formation of social ties and social networks. This generalized weighted social network (GWSN) model incorporates triadic closure, homophilic interactions, and various link termination mechanisms, which have been studied separately in the previousworks. Accordingly, the GWSN model has an increased number of input parameters and the model behavior gets excessively complex, making it challenging to clarify the model behavior. We have executed massive simulations with a supercomputer and used the results as the training data for deep neural networks to conduct regression analysis for predicting the properties of the generated networks from the input parameters. The obtained regression model was also used for global sensitivity analysis to identify which parameters are influential or insignificant. We believe that this methodology is applicable for a large class of complex network models, thus opening the way for more realistic quantitative agent-based modeling. ; Peer reviewed