Learning Dynamic Network Models for Complex Social Systems

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 problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in community membership; 3) using time series to predict changes in network structure; 4) modeling coevolution patterns across network layers and 5) extracting information from negative layers of a multiplex network. 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\u27 behavior, we constructed multiplex networks with link types for raid, communication, and trading. The contributions of this dissertation include 1) extensive experiments on the dynamics of networks formed from diverse social processes; 2) new game theoretic models for community detection in dynamic networks; 3) supervised and unsupervised methods for link prediction in multiplex coevolving networks for both positive and negative links. We demonstrate that our holistic approach for modeling network dynamics in coevolving, multiplex networks outperforms factored methods that separately consider temporal and cross-layer patterns

Conflict and Communication in Massively-Multiplayer Online Games

Massively-multiplayer online games (MMOGs) can serve asa unique laboratory for studying large-scale human behaviors. However,one question that often arises is whether the observed behavior is specificto the game world and its winning conditions. This paper studiesthe nature of conflict and communication across two game worlds thathave different game objectives. We compare and contrast the structureof attack networks with trade and communication networks. Similar toreal-life, social structures play a significant role in the likelihood of interplayerconflict

Conflict And Communication In Massively-Multiplayer Online Games

Massively-multiplayer online games (MMOGs) can serve asa unique laboratory for studying large-scale human behaviors. However,one question that often arises is whether the observed behavior is specificto the game world and its winning conditions. This paper studiesthe nature of conflict and communication across two game worlds thathave different game objectives. We compare and contrast the structureof attack networks with trade and communication networks. Similar toreal-life, social structures play a significant role in the likelihood of interplayerconflict