Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

Abstract

Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

“…We have not yet considered the complexity of competition among different mobile applications. In addition, as in many field of multi-agent models, only empirical data are verified in agent-based models with different social networks [34,52]. Although the empirical analysis has not been carried out, we will employ the real data and conclusions collected from empirical research to confirm the rationality of the parameter setting in future research.…”

“…Numerous studies indicate that social network analysis is a common tool to describe realistic consumer networks. It can effectively describe the diffusion environment of user interaction [4,34]. Prior studies constructed the consumers' relationship network based on agent-based modeling and social network analysis, helping firms predict product proliferation in different networks [35,36].…”

“…For example, agents are aware of the impacts of each connection and decide whether they establish relations with other agents. Therefore, consumers' adoption decisions are influenced by marketing activities as well as positive and negative word-of-mouth communication between neighboring consumers in the social networks [34,42]. In this paper, we explore the influence of social network structures on the decision-making of mobile user groups, investigating the characteristics of innovation diffusion for mobile application under global and local network effects.…”

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

“…Integrating these methods presents a proper view of human behavior. 3,28 Several methods present approaches for marketing based on user behavior in the graph. These methods developed the recommendation system.…”

“…Shared followers can detect the relationship between individuals of a community among those people. 3 In these methods, the behavior of people cannot be traced based on the following of other people. Discovering shared ideas between people, along with shared followers, can create communities with the same ideas.…”