A study on coevolutionary dynamics of knowledge diffusion and social network structure. Issue 7 (1st May 2015)

• Record Type: Journal Article
• Authors: Luo, Shuangling; Du, Yanyan; Liu, Peng; Xuan, Zhaoguo; Wang, Yanzhang
• Is Part Of: Expert systems with applications. Volume 42:Issue 7(2015)
• Page Start: 3619
• Page End: 3633
• Publication Date: 2015-05-01
• Subjects: Knowledge diffusion -- Network structure -- Coevolutionary dynamics -- Knowledge distance -- Agent-based modeling; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.12.038
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A model is proposed to study the coevolution of network and knowledge. The network evolves from random to small-world, when knowledge diversity remains. The small-world diminishes with the reduction of knowledge-transfer effectiveness. Strong cohesion can lead to network separation, and reduce long-term transfer. Abstract: Knowledge diffusion in social networks has extensively been studied in the communities of knowledge and innovation management and of complex networks. However, less attention has been paid on the coevolution of knowledge and network. In this work an agent-based model is proposed to study such coevolutionary dynamics. A set of agents, which are initially interconnected to form a random network, either exchange knowledge with their neighbors or move toward a new location through an edge-rewiring procedure. The activity of knowledge exchange between agents is determined by a knowledge transfer rule that two connecting agents exchange knowledge only if their knowledge distance is less than a given threshold. What's more, within the threshold, knowledge exchange is more effective when the knowledge distance is greater. The activity of agent movement is determined by a neighborhood adjustment rule that one agent may move toward a remote location or reside in the local cluster. Through simulative analysis of this model, some interesting phenomena are observed. Essentially, the bi-directional influences between knowledge transfer and neighborhood adjustment give rise to the coevolution of the network structure and the diffusion of knowledge at the global level. In particular, the rise and fall of "small-world" structure of the network can be observed during the process of knowledge transfer.