Using Massively Multiplayer Online Game Data to Analyze the Dynamics of Social Interactions

Human societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this issue, our approach is to explicitly learn the dynamics of these complex networks. Our research focuses on two problems: 1) analyzing the effects of aggression and cooperation on the network structure and 2) modeling link formation patterns across network layers. To study these problems, we created a rich dataset extracted from observing social interactions in the massively multiplayer online game Travian. Most online social media platforms are optimized to support a limited range of social interactions, primarily focusing on communication and information sharing. In contrast, relations in massively-multiplayer online games (MMOGs) are often formed during the course of gameplay and evolve as the game progresses. To analyze the players’ behavior, we constructed multiplex networks with link types for attack, communication, and trading. In this chapter, we illustrate the versatility of the Travian dataset with case studies of how to analyze different aspects of social network dynamics.