Online multiplayer games are a popular form of social interaction, used by hundreds of millions of individuals. However, little is known about the social networks within these online games, or how they evolve over time. Understanding human social dynamics within massive online games can shed new light on social interactions in general and inform the development of more engaging systems. Here, we study a novel, large friendship network, inferred from nearly 18 billion social interactions over 44 weeks between 17 million individuals in the popular online game Halo:Reach. This network is one of the largest, most detailed temporal interaction networks studied to date, and provides a novel perspective on the dynamics of online friendship networks, as opposed to mere interaction graphs. Initially, this network exhibits strong structural turnover and decays rapidly from a peak size. In the following period, however, both network size and turnover stabilize, producing a dynamic structural equilibrium. In contrast to other studies, we nd that the Halo friendship network is non-densifying: both the mean degree and the average pairwise distance are stable, suggesting that densication cannot occur when maintaining friendships is costly. Finally, players with greater long-term engagement exhibit stronger local clustering, suggesting a group-level social engagement process. These results demonstrate the utility of online games for studying social networks, shed new light on empirical temporal graph patterns, and clarify the claims of universality of network densication.
Lastly, we would be remiss if we did not also mention work related to the study of online games. Most uses of online game data have focused on understanding certain aspects of human social behavior in online environments. Examples include individual and team performance @cite_33 @cite_30 @cite_11 @cite_10 , expert behavior @cite_16 @cite_18 , homophily @cite_20 , group formation @cite_27 , economic activity @cite_5 @cite_31 , and deviant behavior @cite_7 . Most of this work has focused on massively multiplayer online role playing games (MMORPGs), e.g., World of Warcraft, although a few have examined social behavior in first person shooter (FPS) games like @cite_11 . Relatively little of this work has focused on the structure and dynamics of social networks.
@cite_30 @cite_18 @cite_33 @cite_7 @cite_27 @cite_5 @cite_31 @cite_16 @cite_10 @cite_20 @cite_11
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In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players. This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players. The prediction models provide a projection of player’s future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools. First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction. Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree. Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage. How do video game skills develop, and what sets the top players apart? We study this question of skill through a rating generated from repeated multiplayer matches called TrueSkill. Using these ratings from 7 months of games from over 3 million players, we look at how play intensity, breaks in play, skill change over time, and other games affect skill. These analyzed factors are then combined to model future skill and games played; the results show that skill change in early matches is a useful metric for modeling future skill, while play intensity explains eventual games played. The best players in the 7-month period, who we call "Master Blasters", have varied skill patterns that often run counter to the trends we see for typical players. The data analysis is supplemented with a 70 person survey to explore how players' self-perceptions compare to the gameplay data; most survey responses align well with the data and provide insight into player beliefs and motivation. Finally, we wrap up with a discussion about hiding skill information from players, and implications for game designers. In this study, we propose a comprehensive performance management tool for measuring and reporting operational activities of game players This study uses performance data of game players in EverQuest II, a popular MMORPG developed by Sony Online Entertainment, to build performance prediction models for game players The prediction models provide a projection of player's future performance based on his past performance, which is expected to be a useful addition to existing player performance monitoring tools First, we show that variations of PECOTA [2] and MARCEL [3], two most popular baseball home run prediction methods, can be used for game player performance prediction Second, we evaluate the effects of varying lengths of past performance and show that past performance can be a good predictor of future performance up to a certain degree Third, we show that game players do not regress towards the mean and that prediction models built on buckets using discretization based on binning and histograms lead to higher prediction coverage. Gold farming refers to the illicit practice of gathering and selling virtual goods in online games for real money. Although around one million gold farmers engage in gold farming related activities, to date a systematic study of identifying gold farmers has not been done. In this paper we use data from the massively-multiplayer online role-playing game (MMORPG) EverQuest II to identify gold farmers. We perform an exploratory logistic regression analysis to identify salient descriptive statistics followed by a machine learning binary classification problem to identify a set of features for classification purposes. Given the cost associated with investigating gold farmers, we also give criteria for evaluating gold farming detection techniques, and provide suggestions for future testing and evaluation techniques. Advanced communication technologies enable strangers to work together on the same tasks or projects in virtual environments. Understanding the formation of task-oriented groups is an important first step to study the dynamics of team collaboration. In this paper, we investigated group combat activities in Sony’s EverQuest II game to identify the role of player and group attributes on group formation. We found that group formation is highly influenced by players’ common interests on challenging tasks. Players with less combat experience are more likely to participate in group events for difficult tasks and team performance is positively correlated to group size. This article proposes an empirical test of whether aggregate economic behavior maps from the real to the virtual. Transaction data from a large commercial virtual world — the first such data set provided to outside researchers — is used to calculate metrics for production, consumption and money supply based on real-world definitions. Movements in these metrics over time were examined for consistency with common theories of macroeconomic change. The results indicated that virtual economic behavior follows real-world patterns. Moreover, a natural experiment occurred, in that a new version of the virtual world with the same rules came online during the study. The new world's macroeconomic aggregates quickly grew to be nearly exact replicas of those of the existing worlds, suggesting that Code is Law': macroeconomic outcomes in a virtual world may be explained largely by design structure. This paper examines social structures underlying economic activity in Second Life (SL), a massively multiplayer virtual world that allows users to create and trade virtual objects and commodities. We find that users conduct many of their transactions both within their social networks and within groups. Using frequency of chat as a proxy of tie strength, we observe that free items are more likely to be exchanged as the strength of the tie increases. Social ties particularly play a significant role in paid transactions for sellers with a moderately sized customer base. We further find that sellers enjoying repeat business are likely to be selling to niche markets, because their customers tend to be contained in a smaller number of groups. But while social structure and interaction can help explain a seller's revenues and repeat business, they provide little information in the forecasting a seller's future performance. Our quantitative analysis is complemented by a novel method of visualizing the transaction activity of a seller, including revenue, customer base growth, and repeat business. We examine the social behaviors of game experts in Everquest II, a popular massive multiplayer online role-playing game (MMO). We rely on Exponential Random Graph Models (ERGM) to examine the anonymous privacy-protected social networks of 1,457 players over a five-day period. We find that those who achieve the most in the game send and receive more communication, while those who perform the most efficiently at the game show no difference in communication behavior from other players. Both achievement and performance experts tend to communicate with those at similar expertise levels, and higher-level experts are more likely to receive communication from other players. In this paper, we report findings from an exploratory study of player and team performance in Halo 3, a popular First-Person-Shooter game developed by Bungie. In the study, we first analyze player and team statistics obtained from the 2008 and 2009 seasons for professional Halo 3 games in order to investigate the impact of change in team composition on player and team performance. We then examine the impact of past performance on future performance of players and teams. Performing a large-scale experiment on a real-world dataset, we observe that player and team performance can be predicted with fairly high accuracy and that information about change in team composition can further improve the prediction results. Virtual space eliminates the constraints of physical distances on communication and interaction. In this study, we examine the impact of offline proximity and homophily of players on their online interactions in EverQuest II. The results show that spatial proximity as well as homophily still influence players’ online behavior. The market for video games has skyrocketed over the past decade. In the United States alone, the video game industry in 2009 generated almost US$20 billion in sales. Furthermore, according to (2008), an estimated 97 of the teenage population and 53 of the adult population are regular game players. Massively multiplayer online games (MMOGs) have become increasingly popular and amassed communities comprised of over 47 million subscribers by the year 2008. MMOGs are online spaces providing users with comprehensive virtual universes, each with its own unique context and mechanics. They range from the fantastical world of elves, dwarfs, and humans to space faring corporations and mirrors of our world. Large numbers of users interact and role-play via in-game mechanics.