Reinforced communication and social navigation: remember your friends and remember yourself

Abstract

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here we introduce a simple agent-based model to study the coupling between peoples' ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is in general easier to recover ideas from the network structure than vice versa.

6 pages, 6 Figures

References in corpus (8)

• Modularity and community structure in networks
• Community detection in graphs
• Nonequilibrium phase transition in the coevolution of networks and opinions
• Consensus formation on adaptive networks
• Emergence of communities in weighted networks
• Who's talking first? Consensus or lack thereof in coevolving opinion formation models
• Opinion and community formation in coevolving networks
• Reinforced communication and social navigation generate groups in model networks