A visual analytics approach for understanding egocentric intimacy network evolution and impact propagation in MMORPGs

Massively Multiplayer Online Role-playing Games (MMORPGs) feature a large number of players socially interacting with one another in an immersive gaming environment. A successful MMORPG should engage players and meet their needs to achieve different categories of gratifications. Research on the evolution of player social interaction network and the dynamics of inter-player intimacy could provide insights into players' gratification-oriented behaviors in MMORPGs. Such understanding could in turn guide game designs for better engaging existing players and marketing strategies for attracting newcomers. Conventional dynamic network analysis may help investigate game-based social interactions at the macroscopic level. However, current dynamic network visualization techniques mainly focus on illustrating topological changes of the entire network, which are unsuitable for analyzing player-specific social interactions in the virtual world from an egocentric perspective. In general, game designers and operators find it difficult to analyze the way players with different gratification needs may interact with one another and the consequences on their relationships with direct ties, using a decentralized social graph with complicated time-varying structures. In this paper, we present MMOSeer, a visual analytics system for exploring the evolution of egocentric player intimacy network. MMOSeer focuses on the relationship between a player (ego) and his/her directly-linked friends (alters). We follow a user-centered design process to develop the system with game analysts and apply novel visualization techniques in conjunction with well-established algorithms to depict the evolution of intimacy egocentric network. We also derive a centrality change metric to infer how the impact of changes in an ego's interactive behaviors may propagate through the intimacy network, reshaping the structure of the alters' social circles at both micro and macro levels. Finally, we validate the usability of MMOSeer by discovering different user interaction patterns and the corresponding ego-network structural changes in a real-world gameplay dataset from a commercial MMORPG.

[1]  Stanford,et al.  Learning to Discover Social Circles in Ego Networks , 2012 .

[2]  Ayellet Tal,et al.  Online Dynamic Graph Drawing , 2008, IEEE Transactions on Visualization and Computer Graphics.

[3]  Mark D. Griffiths,et al.  Social Interactions in Massively Multiplayer Online Role-Playing Gamers , 2007, Cyberpsychology Behav. Soc. Netw..

[4]  G. Ahuja Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study , 1998 .

[5]  J. Kruskal Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[6]  James C. Hyatt,et al.  Maslow’s Hierarchy of Needs , 2018 .

[7]  Georg Groh Interactively Visualizing Dynamic Social Networks with DySoN , 2009 .

[8]  Michael Burch,et al.  Layered TimeRadarTrees , 2011, 2011 15th International Conference on Information Visualisation.

[9]  Benjamin Schmidt,et al.  A Matrix-Based Visualization for Exploring Dynamic Compound Digraphs , 2013, 2013 17th International Conference on Information Visualisation.

[10]  Jon M. Kleinberg,et al.  Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook , 2013, CSCW.

[11]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[12]  Michael Burch,et al.  The State of the Art in Visualizing Dynamic Graphs , 2014, EuroVis.

[13]  Chen Wang,et al.  Dynamic network visualization in 1.5D , 2011, 2011 IEEE Pacific Visualization Symposium.

[14]  Jean-Daniel Fekete,et al.  Visualizing dynamic networks with matrix cubes , 2014, CHI.

[15]  LeskovecJure,et al.  Discovering social circles in ego networks , 2014 .

[16]  Robert J. Moore,et al.  The life and death of online gaming communities: a look at guilds in world of warcraft , 2007, CHI.

[17]  Christian Bauckhage,et al.  Analyzing the Evolution of Social Groups in World of Warcraft® , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[18]  Padhraic Smyth,et al.  Dynamic Egocentric Models for Citation Networks , 2011, ICML.

[19]  Lorrie Faith Cranor,et al.  A Framework for Reasoning About the Human in the Loop , 2008, UPSEC.

[20]  Quan Li,et al.  Animated narrative visualization for video clickstream data , 2016, SIGGRAPH Asia Symposium on Visualization.

[21]  Jean-Daniel Fekete,et al.  GraphDiaries: Animated Transitions andTemporal Navigation for Dynamic Networks , 2014, IEEE Transactions on Visualization and Computer Graphics.

[22]  Chee Siang Ang,et al.  SOCIAL ROLES OF PLAYERS IN MMORPG GUILDS , 2010 .

[23]  Aki Hayashi,et al.  Initial Positioning Method for Online and Real-Time Dynamic Graph Drawing of Time Varying Data , 2013, 2013 17th International Conference on Information Visualisation.

[24]  Stephan Diehl,et al.  Preserving the Mental Map using Foresighted Layout , 2001, VisSym.

[25]  Thomas E. Gorochowski,et al.  Using Aging to Visually Uncover Evolutionary Processes on Networks , 2012, IEEE Transactions on Visualization and Computer Graphics.

[26]  Robin I. M. Dunbar,et al.  Social network size in humans , 2003, Human nature.

[27]  Davide Gazzè,et al.  Towards a Characterization of Egocentric Networks in Online Social Networks , 2011, OTM Workshops.

[28]  C. Prell Social Network Analysis: History, Theory and Methodology , 2011 .

[29]  A. Maslow,et al.  Maslow’s Hierarchy of Needs , 2016 .

[30]  Wayne Buente,et al.  Selling out the magic circle: free-toplay games and developer ethics , 2016, DiGRA/FDG.

[31]  Danai Koutra,et al.  NetSimile: A Scalable Approach to Size-Independent Network Similarity , 2012, ArXiv.

[32]  Chuen-Tsai Sun,et al.  Player Guild Dynamics and Evolution in Massively Multiplayer Online Games , 2008, Cyberpsychology Behav. Soc. Netw..

[33]  Mathias Pohl,et al.  As time goes by: integrated visualization and analysis of dynamic networks , 2008, AVI '08.

[34]  Kon Shing Kenneth Chung,et al.  Egocentric analysis of co-authorship network structure, position and performance , 2012, Inf. Process. Manag..

[35]  Lucy T. Nowell,et al.  Change blindness in information visualization: a case study , 2001, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001..

[36]  Jarke J. van Wijk,et al.  Dynamic Network Visualization withExtended Massive Sequence Views , 2014, IEEE Transactions on Visualization and Computer Graphics.

[37]  Aaron Quigley,et al.  Exploring temporal ego networks using small multiples and tree-ring layouts , 2011, ACHI 2011.

[38]  David Kirschner,et al.  Structural Roles in Massively Multiplayer Online Games: A Case Study of Guild and Raid Leaders in World of Warcraft , 2014 .

[39]  Jian Zhao,et al.  egoSlider: Visual Analysis of Egocentric Network Evolution , 2016, IEEE Transactions on Visualization and Computer Graphics.

[40]  Thomas Schank,et al.  Dynamic graph drawing in visone , 2008 .

[41]  Silvia Miksch,et al.  Visual Analysis of Dynamic Networks Using Change Centrality , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[42]  Mathias Pohl,et al.  Interactive Exploration of Large Dynamic Networks , 2008, VISUAL.

[43]  R. Burt Structural Holes versus Network Closure as Social Capital , 2001 .

[44]  Jure Leskovec,et al.  Discovering social circles in ego networks , 2012, ACM Trans. Knowl. Discov. Data.

[45]  Marco Conti,et al.  Dynamics of personal social relationships in online social networks: a study on twitter , 2013, COSN '13.

[46]  Quan Li,et al.  A Visual Analytics Approach for Understanding Reasons behind Snowballing and Comeback in MOBA Games , 2017, IEEE Transactions on Visualization and Computer Graphics.