Understanding and Recommending Play Relationships in Online Social Gaming

Online Social Networking (OSN) applications such as Facebook's communication and Zynga's gaming platforms service hundreds of millions of users. To understand and model such relationships, social network graphs are extracted from running OSN applications and subsequently processed using social and complex network analysis tools. In this paper, we focus on the application domain of Online Social Games (OSGs) and deploy a formalism for extracting graphs from large datasets. Our formalism covers notions such as game participation, adversarial relationships, match outcomes, and allows to filter out "weak" links based on one or more threshold values. Using two novel large-scale OSG datasets, we investigate a range of threshold values and their influence on the resulting OSG graph properties. We discuss how an analysis of multiple graphs-obtained through different extraction rules-could be used in an algorithm to improve matchmaking for players.