Dynamics of clustered opinions in complex networks

Abstract

A simple model for simulating tug of war game as varying the player number in a team is discussed to identify the slow pace of fast change. This model shows that a large number of information sources leads slow change for the system. Also, we introduce an opinion diffusion model including the effect of a high degree of clustering. This model shows that the de facto standard and lock-in effect, well-known phenomena in economics and business management, can be explained by the network clusters.

Stocks of information in personal consumption: a network model with non-rival borrowing and content overlap

“…Clustering is the well-recognized property of regular and small world networks for the levels or states of a construct to be related by the proximity of network members (e.g., Dorogovtsev 2004;Jackson and Rogers 2007;Jung et al 2008). Watts and Strogatz (1998) measure of a clustering-related measure (cliquishness) can be defined as follows.…”

The Naming Game in social networks: community formation and consensus engineering

“…Opinion spreading and social dynamics (Durlauf, 1999;Castellano et al, 2008) on regular and random networks are examples of the latter. A large number of studies have investigated models of opinion dynamics Ben-Naim, 2005;Deffuant et al, 2000;Hegselmann and Krause, 2002;Lorenz, 2007Lorenz, , 2008; Krapivsky and Redner, 2003;Sood and Redner, 2005;Sznajd-Weron and Sznajd, 2000;Kozma and Barrat, 2008a;Benczik et al, 2008;Antal et al, 2005;Jung et al, 2008) and the dissemination of culture (Axelrod, 1997;San Miguel et al, 2005;Mazzitello et al, 2007), while fundamental models for residential and ethnic segregation have also attracted strong interest (Schelling, 1971;Zhang, 2004;Vinkovic and Kirman, 2006;Lim et al, 2007). Most recently, researchers have also turned their focus to models where both the network topology and opinions change over time (Kozma and Barrat, 2008a,b).…”