Skip to main content Accessibility help
×
Hostname: page-component-7dd5485656-glrdx Total loading time: 0 Render date: 2025-10-30T20:09:51.838Z Has data issue: false hasContentIssue false

Part IV - Techniques for Analyzing Game Data

Published online by Cambridge University Press:  15 June 2018

Kiran Lakkaraju
Affiliation:
Sandia National Laboratories, New Mexico
Gita Sukthankar
Affiliation:
University of Central Florida
Rolf T. Wigand
Affiliation:
University of Arkansas
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

Type
Chapter
Information
Social Interactions in Virtual Worlds
An Interdisciplinary Perspective
, pp. 311 - 312
Publisher: Cambridge University Press
Print publication year: 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

References

Abulrub, A.-H. G., Attridge, A., & Williams, M. A. (2011). Virtual reality in engineering education: The future of creative learning. iJET, 6(4), 411.Google Scholar
Antila, J., & Lakkakorpi, J. (2003). On the effect of reduced quality of service in multiplayer on-line games. International Journal of Intelligent Games & Simulation, 2(2), 8995.Google Scholar
Dias, E. (2014). A model to evaluate QoE of online social gaming. MSc thesis, Delft University of Technology.Google Scholar
Drain, B. (2008). EVE evolved: EVE Online's server model. Retrieved from: http://massively.joystiq.com/2008/09/28/eve-evolved-eve-onlines-server-model/ (accessed October 11, 2017).Google Scholar
Funk, J. (2013). MOBA, DOTA, ARTS: A brief introduction to gaming's biggest, most impenetrable genre. Retrieved from: www.polygon.com/2013/9/2/4672920/moba-dota-arts-a-brief-introduction-to-gamings-biggest-most (accessed October 11, 2017).Google Scholar
Guo, Y., & Iosup, A. (2012). The Game Trace archive. In 11th International Workshop on Network and Systems Support for Games (NetGames) 2012: 1–6, Venice, Italy.Google Scholar
Iosup, A., Shen, S., Guo, Y., Hugtenburg, S., Donkervliet, J., & Prodan, R. (2014). Massivizing online games using cloud computing: A vision. In Multimedia and Expo Workshops (ICMEW), 14.10.1109/ICMEW.2014.6890684CrossRefGoogle Scholar
Jia, A. Lu, Shen, S., van de Bovenkamp, R., Iosup, A., Kuipers, F. A., & Epema, D. H. J. (2015, October). Socializing by gaming: Revealing social relationships in multiplayer online games. ACM Transactions on Knowledge Discovery from Data, 10(2), 11:1–11:29CrossRefGoogle Scholar
Kuipers, F., Kooij, R., De Vleeschauwer, D., & Brunnstrom, K. (2010). Techniques for measuring quality of experience. In Proceedings of the 8th International Conference on Wired/Wireless Internet Communications (WWIC’10), Luleå, Sweden.CrossRefGoogle Scholar
Lien, T. (2014, August 11). What if video games could help us flirt? Retrieved from: www.polygon.com/2014/8/11/5990319/game-oven-bounden-flirt (accessed October 11, 2017).Google Scholar
Liu, E. S., & Theodoropoulos, G. K. (2014). Space-time matching algorithms for interest management in distributed virtual environments. ACM Transactions on Modeling and Computer Simulation, 24(3), 123.Google Scholar
Märtens, M., Shen, S., Iosup, A., & Kuipers, F. A. (2015). Toxicity detection in multiplayer online games. In Proceedings of 14th International Workshop on Network and Systems Support for Games (NetGames), Zagreb, Croatia.CrossRefGoogle Scholar
McCormick, R. (2013). ‘League of Legends’ eSports finals watched by 32 million people. Retrieved from:www.theverge.com/2013/11/19/5123724/league-of-legends-world-championship-32-million-viewers (accessed October 11, 2017).Google Scholar
McGonigal, J. (2011). Reality is broken: Why games make us better and how they can change the world. London: Jonathan Cape.Google Scholar
Morse, K. L. (1996). Interest management in large-scale distributed simulations. Technical Report. Information and Computer Science, University of California, Irvine.Google Scholar
Nae, V., Iosup, A., & Prodan, R. (2011). Dynamic resource provisioning in massively multiplayer online games. IEEE Transactions on Parallel and Distributed Systems, 22(3), 380395.10.1109/TPDS.2010.82CrossRefGoogle Scholar
Newzoo team. (2016). Global eSports market report. Reports an audience of over 130 million esports. Retrieved from: https://newzoo.com/insights/countries/global/ (accessed October 11, 2017).Google Scholar
Ries, M., Svoboda, P., & Rupp, M (2008, June). Empirical study of subjective quality for massive multiplayer games. In Proceedings of the 15th International Conference on Systems, Signals and Image Processing, Bartislava, Slovakia.10.1109/IWSSIP.2008.4604397CrossRefGoogle Scholar
Shen, S., Hu, S-Y., Iosup, A., & Epema, D. H. J. (2015). Area of simulation: Mechanism and architecture for multi-avatar virtual environments. TOMCCAP, 12(1), 8.10.1145/2764463CrossRefGoogle Scholar
Steam team (2016, February 28). Steam and game stats. Continuously updated numbers indicate millions of online players, from a base of over 100 million players. Retrieved from: http://store.steampowered.com/stats/ (accessed October 11, 2017).Google Scholar
Susi, T., Johannesson, M., & Backlund, P. (2007). Serious games – An overview. Technical Report HS-IKI-TR-07-001. School of Humanities and Informatics University of Skövde, Sweden.Google Scholar
Walker, W. E., Giddings, J., & Armstrong, S. (2011). Training and learning for crisis management using a virtual simulation/gaming environment. Cognition, Technology & Work, 13(3), 163173.CrossRefGoogle Scholar
Wattimena, A. F., Kooij, R. E., van Vugt, J. M., & Ahmed, O. K. (2006). Predicting the perceived quality of a first person shooter: The quake iv g-model. In Proceedings of NetGames, New York, NY.Google Scholar
Wilson, C., Boe, B., Sala, A., Puttaswamy, K. P. N., & Zhao, B. Y. (2009). User interactions in social networks and their implications,” In Proceedings of the 4th ACM European conference on computer systems (EuroSys), Nuremberg, Germany.10.1145/1519065.1519089CrossRefGoogle Scholar
World of Warcraft team. (2015, August). Expansion features for The Legion Awaits. Mentions “in-game communities,” “social groups,” and attention to “form the perfect group to play your way.” Retrieved from: http://eu.battle.net/wow/en/legion/#features (accessed October 11, 2017).Google Scholar

References

Bader, B. W., Harshinan, R. A., & Kolda, T. G. (2007). Temporal analysis of semantic graphs using ASALSAN. In Proceedings of IEEE ICDM.Google Scholar
Bartle, R. (1996). Hearts, clubs, diamonds, spades: Players who suit MUDs. Journal of MUD Research, 1(1).Google Scholar
Bateman, C. M., & Boon, R. (2006). 21st century game design. Newton Center, MA: Charles River Media.Google Scholar
Bauckhage, C. (2015). k-Means clustering is matrix factorization. arXiv preprint arXiv:1512.07548.Google Scholar
Bauckhage, C., & Sifa, R. (2015). k-Maxoids clustering. In Proceedings of KDML-LWA.Google Scholar
Bauckhage, C., & Thurau, C. (2004). Towards a fair ’n square aimbot using mixtures of experts to learn context aware weapon handling. In Proceedings of GAME-ON.Google Scholar
Bauckhage, C., & Thurau, C. (2009). Making archetypal analysis practical. In Pattern Recognition. Lecture Notes in Computer Science, Vol. 5748. New York: Springer Science+Business Media.Google Scholar
Bauckhage, C., Kersting, K., Sifa, R., Thurau, C, Drachen, A., & Canossa, A. (2012). How players lose interest in playing a game: An empirical study based on distributions of total playing times. In Proceedings of IEEE CIG.10.1109/CIG.2012.6374148CrossRefGoogle Scholar
Bauckhage, C., Sifa, R., Drachen, A., Thurau, C, & Hadiji, F. (2014). Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels. In Proceedings of IEEE CIG.Google Scholar
Bauckhage, C., Drachen, A., & Sifa, R. (2015). Clustering game behavior data. IEEE Transactions on Computational Intelligence and AI in Games, 7(3), 266278.10.1109/TCIAIG.2014.2376982CrossRefGoogle Scholar
Bohannon, J. (2010). Game-miners grapple with massive data. Science, 330(6000), 3031.Google ScholarPubMed
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 532.CrossRefGoogle Scholar
Campbell, J., Tremblay, J., & Verbrugge, C. (2015). Clustering player paths. In Proceedings of FDG.Google Scholar
Canossa, A., & Drachen, A. (2009a). Patterns of play: Play-personas in user-centered game development. In Proceedings of DIGRA.Google Scholar
Canossa, A., & Drachen, A. (2009b). Play-personas: Behaviors and belief systems in user-centered game design. In Proceedings of ACM INTERACT.Google Scholar
Canossa, A., Drachen, A., & Sorensen, J. (2011). Arrrgghh!!!: Blending quantitative and qualitative methods to detect player frustration. In Proceedings of FDG.Google Scholar
Canossa, A., Martinez, J. B., & Togelius, J. (2013). Give me a reason to dig Minecraft and psychology of motivation. In Proceedings of IEEE CIG.Google Scholar
Chawla, N., Bowyer, K., Hall, L. O, & Kegelmeyer, W. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(1), 321357.10.1613/jair.953CrossRefGoogle Scholar
Cutler, A., & Breiman, L. (1994). Archetypal analysis. Technometrics, 36(4), 338347.CrossRefGoogle Scholar
Drachen, A. (2014). Behavioral profiling in game user research. Presentation at the 4th International Game Developers Association Game User Research Summit.Google Scholar
Drachen, A., & Canossa, A. (2009). Analyzing spatial user behavior in computer games using Geographic Information Systems. In Proceedings of MindTrek.10.1145/1621841.1621875CrossRefGoogle Scholar
Drachen, A., & Canossa, A. (2011). Evaluating motion: Spatial user behaviour in virtual environments. International Journal of Arts and Technology, 4(2), 294314.10.1504/IJART.2011.041483CrossRefGoogle Scholar
Drachen, A., & Schubert, M. (2013). Spatial game analytics. In El-Nasr, M. S., Drachen, A., & Canossa, A. (eds.), Game analytics: Maximizing the value of player data (pp. 365402). New York: Springer Science+Business Media.CrossRefGoogle Scholar
Drachen, A., Yannakakis, G. N., Canossa, A., & Togelius, J. (2009). Player modeling using self-organization in tomb raider: Underworld. In Proceedings of IEEE CIG.Google Scholar
Drachen, A., Sifa, R., Bauckhage, C., & Thurau, C. (2012). Guns, swords and data: Clustering of player behavior in computer games in the wild. In Proceedings of IEEE CIG.Google Scholar
Drachen, A., Thurau, C., Sifa, R., & Bauckhage, C. (2013a). A comparison of methods for player clustering via behavioral telemetry. In Proceedings of FDG.Google Scholar
Drachen, A., Thurau, C., Togelius, J., Yannakakis, G., & Bauckhage, C. (2013b). Game data mining. In El-Nasr, M. S., Drachen, A., & Canossa, A. (eds.), Game analytics: Maximizing the value of player data. New York: Springer Science+Business Media.Google Scholar
Drachen, A., Baskin, S., Riley, J., & Klabjan, D. (2014a). Going out of business: Auction house behavior in the massively multi-player online game glitch. Entertainment Computing, 5(4), 219232.CrossRefGoogle Scholar
Drachen, A., Sifa, R., & Thurau, C. (2014b). The name in the game: Patterns in character names and gamer tags. Entertainment Computing, 5(1), 2132.10.1016/j.entcom.2014.02.001CrossRefGoogle Scholar
Drachen, A., Yancey, M., Maquire, J., Chu, D., Wang, Y. I., Mahlman, T., Schubert, M., & Klabjan, D. (2014c). Skill-based differences in spatio-temporal team behaviour in defence of The Ancients 2 (DotA 2). In Proceedings of the IEEE Consumer Electronics Society Games, Entertainment, Media Conference.Google Scholar
Eggert, C., Herrlich, M., Smeddinck, J., & Malaka, R. (2015). Classification of player roles in the team-based multi-player game Dota 2. In Proceedings of Entertainment Computing.Google Scholar
El-Nasr, M. S., Drachen, A., & Canossa, A. (2013). Game analytics: Maximizing the value of player data. New York: Springer Science+Business Media.10.1007/978-1-4471-4769-5CrossRefGoogle Scholar
Feng, J., Brandt, D., & Saha, D. (2007). A long-term study of a popular MMORPG. In Proceedings ACM SIGCOMM WNSSG.Google Scholar
Fields, T., & Cotton, B. (2011). Social game design: Monetization methods and mechanics. San Mateo, CA: Morgan Kaufmann.10.1201/9780240817675CrossRefGoogle Scholar
Gao, L., Judd, J., Wong, D., & Lowder, J. (2013). Classifying dota 2 hero characters based on play style and performance. Retrieved from: http://spotidoc.com/doc/163929/classifying-dota-2-heroes-based-on-play-style-and-performGoogle Scholar
Hadiji, F., Sifa, R., Drachen, A., Thurau, C., Kersting, K., & Bauckhage, C. (2014). Predicting player churn in the wild. In Proceedings of IEEE CIG.Google Scholar
Harshman, R. A. (1978). Models for analysis of asymmetrical relationships among N objects or stimuli. In Proceedings of the Joint Meeting of the Psychometric Society and the Society for Mathematical Psychology.Google Scholar
Holmgard, C., Liapis, A., Togelius, J., & Yannakakis, G. N. (2015). Monte-Carlo tree search for persona based player modeling. In Proceedings of AIIDE Player Modeling Workshop.Google Scholar
Hoobler, N., Humphreys, G., & Agrawala, M. (2004). Visualizing competitive behaviors in multi-user virtual environments. In Proceedings of VIS.Google Scholar
Kersting, K., Wahabzada, M., Thurau, C., & Bauckhage, C. (2010). Hierarchical convex NMF for clustering massive data. In Proceedings of ACML.Google Scholar
Kiers, H. A. L. (1997). DESICOM: Decomposition of asymmetric relationships data into simple components. Behaviormetrika, 24(2), 203217.10.2333/bhmk.24.203CrossRefGoogle Scholar
Kim, J. H., Gunn, D. V., Schuh, E., Phillips, B. C., Pagulayan, R. J., & Wixon, D. (2008). Tracking real-time user experience (true): A comprehensive instrumentation solution for complex systems. In Proceedings of ACM CHI.Google Scholar
Knowles, I., Castronova, E., & Ross, T. (2015). Virtual economies: Origins and issues. The international encyclopedia of digital communication and society.10.1002/9781118767771.wbiedcs046CrossRefGoogle Scholar
Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. In Proceedings of ECML.Google Scholar
Laviers, K., Sukthankar, G., Molineaux, M., & Aha, D. (2009). Improving offensive performance through opponent modeling. In Proceedings of AAAI AIIDE.Google Scholar
Lim, C., & Harrell, D. F. (2015). Revealing social identity phenomena in videogames with archetypal analysis. In Proceedings of AISB.Google Scholar
Lim, N. (2012). Freemium games are not normal. Retrieved from: www.gamasutra.com/blogs/NickLim/.Google Scholar
Luton, W. (2013). Free-to-play: Making money from games you give away. New Riders.Google Scholar
Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., & Yannakakis, G. N. (2010). Predicting player behavior in Tomb Raider: Underworld. In Proceedings of IEEE GIG.Google Scholar
Mellon, L. (2009). Applying metrics driven development to MMO costs and risks, http://maggotranch.com/.Google Scholar
Miller, J.L., & Crowcroft, J. (2010). Group movement in World of Warcraft battlegrounds. International Journal of Advanced Media and Communication, 4(4), 387404.10.1504/IJAMC.2010.036837CrossRefGoogle Scholar
Mitchell, T. M. (1997). Machine learning. New York, NY: McGraw-Hill.Google Scholar
Morup, M., & Hansen, L. K. (2012). Archetypal analysis for machine learning and data mining. Neurocomputing, 80(March), 5463.10.1016/j.neucom.2011.06.033CrossRefGoogle Scholar
Müller, S., Kapadia, M., Prey, S., et al. (2015). Statistical analysis of player behavior in minecraft. In Proceedings of FDG.Google Scholar
Nacke, L. E., Bateman, C, & Mandryk, R. L. (2014). BrainHex: A neurobio-logical gamer typology survey. Entertainment Computing, 5(1), 5562.10.1016/j.entcom.2013.06.002CrossRefGoogle Scholar
Normoyle, A., & Jensen, S. T. (2015). Bayesian clustering of player styles for multiplayer games. In Proceedings of AAAI AIIDE.Google Scholar
Nozhnin, D. (2012). Predicting churn: Data-mining your game. Gamasutra.Google Scholar
Nozhnin, D. (2013). Predicting churn: When do veterans quit? Gamasutra.Google Scholar
Ong, H. Y., Deolalikar, S., & Penge, M. V. (2015). Player behavior and optimal team composition in online multiplayer games. Retrieved from: http://arxiv.org/abs/1503.02230.Google Scholar
Pittman, D., & GauthierDickey, C. (2010). Characterizing virtual populations in massively multiplayer online role-playing games. In Proceedings of MMM.10.1007/978-3-642-11301-7_12CrossRefGoogle Scholar
Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4(1), 7790.CrossRefGoogle Scholar
Raghu, T. S., Kannan, H. R., Rao, A., & Winston, B. (2001). Dynamic profiling of consumers for customized offerings over the Internet: A model and analysis. Decision Support Systems, 32(2), 117134.CrossRefGoogle Scholar
Rioult, R., Metivier, J.-P., Helleu, B., et al. (2014). Mining tracks of competitive video games. In Proceedings of AASRI Conference on Sports Engineering and Computer Science.10.1016/j.aasri.2014.08.014CrossRefGoogle Scholar
Rokach, L., & Maimon, O. (2008). Data mining with decision trees: Theory and applications. Singapore: World Scientific.Google Scholar
Runge, J. (2014). Predictive analytics set to become more valuable in light of rising CPIs. http://www.gamasutra.com/blogs/.Google Scholar
Runge, J., Gao, P., Garcin, F., & Faltings, B. (2014). Churn prediction for high-value players in casual social games. In Proceedings of IEEE CIG.CrossRefGoogle Scholar
Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A Self-Determination Theory approach. Motivation Emotion, 30(4), 344360.10.1007/s11031-006-9051-8CrossRefGoogle Scholar
Sifa, R., & Bauckhage, C. (2013). Archetypical motion: Supervised game behavior learning with archetypal analysis. In Proceedings of IEEE CIG.10.1109/CIG.2013.6633609CrossRefGoogle Scholar
Sifa, R., Drachen, A., Bauckhage, C., Thurau, C., & Canossa, A. (2013). Behavior evolution in Tomb Raider underworld. In Proceedings of IEEE CIG.10.1109/CIG.2013.6633637CrossRefGoogle Scholar
Sifa, R., Bauckhage, C., & Drachen, A. (2014a). Archetypal game recommender systems. In Proceedings of KDML-LWA.Google Scholar
Sifa, R., Bauckhage, C., & Drachen, A. (2014b). The playtime principle: Large-scale cross-games interest modeling. In Proceedings of IEEE CIG.10.1109/CIG.2014.6932906CrossRefGoogle Scholar
Sifa, R., Drachen, A., & Bauckhage, C. (2015a). Large-scale cross-game player behavior analysis on steam. In Proceedings of AAAI AIIDE.Google Scholar
Sifa, R., Hadiji, F., Runge, J., Drachen, A., Kersting, K., & Bauckhage, C. (2015b). Predicting purchase decisions in mobile free-to-play games. In Proceedings of AAAI AIIDE.Google Scholar
Sifa, R., Ojeda, C., & Bauckhage, C. (2015c). User churn migration analysis with DEDICOM. In Proceedings of ACM RccSys.10.1145/2792838.2799680CrossRefGoogle Scholar
Sifa, R., Srikanth, S., Drachen, A., Ojeda, C., & Bauckhage, C. (2016). Predicting retention in sandbox games with tensor factorization-based representation learning. In Proceedings of IEEE CIG.CrossRefGoogle Scholar
Solomon, M. R. (2014). Consumer behavior: Buying, having, and being. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Southey, F., Xiao, G., Holte, R. C., Trommelen, M., & Buchanan, J. (2005). Semi-automated gameplay analysis by machine learning. In Proceedings of AAAI AIIDE.Google Scholar
Spronck, P., Balemans, I., & van Lankveld, G. (2012). Player profiling with Fallout 3. In Proceedings of AAAI AIIDE.Google Scholar
Suznjevic, M., Stupar, I., & Matijasevic, M. (2011). MMORPG player behavior model based on player action categories. In Proceedings of NetGames.10.1109/NetGames.2011.6080982CrossRefGoogle Scholar
Tastan, B., Chang, Y., & Sukthankar, G. (2012). Learning to intercept opponents in first person shooter games. In Proceedings of IEEE CIG.CrossRefGoogle Scholar
Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137155.10.1016/0167-8116(94)00019-KCrossRefGoogle Scholar
Thawonmas, R., & Iizuka, K. (2008). Visualization of online game players based on their action behaviors. International Journal of Computer Games Technology, 2008(Jan.), 906931.10.1155/2008/906931CrossRefGoogle Scholar
Thawonmas, R., Yoshida, K., Lou, J.-K., & Chen, K.-T. (2011). Analysis of revisitations in online games. Entertainment Computing, 2(4), 215221.10.1016/j.entcom.2011.01.003CrossRefGoogle Scholar
Thompson, C. (2007, September). Halo 3: How Microsoft labs invented a new science of play. Wired Magazine.Google Scholar
Thurau, C., Bauckhage, C., & Sagerer, G. (2004). Synthesizing movements for computer game characters. In Joint Pattern Recognition Symposium.CrossRefGoogle Scholar
Thurau, C., Kersting, K., & Bauckhage, C. (2009). Convex non-negative matrix factorization in the wild. In Proceedings of IEEE International Conference on Data Mining.CrossRefGoogle Scholar
Thurau, C., Kersting, K., & Bauckhage, C. (2010). Yes we can: Simplex volume maximization for descriptive web-scale matrix factorization. In Proceedings of ACM CIKM.10.1145/1871437.1871729CrossRefGoogle Scholar
van Lankveld, G., Spronck, P., Van Den Herik, J., & Arntz, A. (2011). Games as personality profiling tools. In Proceedings of IEEE CIG.10.1109/CIG.2011.6032007CrossRefGoogle Scholar
Weber, B., & Mateas, M. (2009). A data mining approach to strategy prediction. In Proceedings of IEEE CIG.10.1109/CIG.2009.5286483CrossRefGoogle Scholar
Weber, B. G., John, M., Mateas, M., & Jhala, A. (2011). Modeling player retention in Madden NFL 11. In Proceedings of IAAI.10.1609/aaai.v25i2.18864CrossRefGoogle Scholar
Wu, X., Kumar, V., Quinlan, J. R., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 137.CrossRefGoogle Scholar
Xie, H., Devlin, S., Kudenko, D., & Cowling, P. (2015). Predicting player disengagement and first purchase with event-frequency based data representation. In Proceedings of IEEE CIG.10.1109/CIG.2015.7317919CrossRefGoogle Scholar
Yang, P. Harrison, B., & Roberts, D. L. (2014). Identifying patterns in combat that are predictive of success in moba games. In Proceedings of FDG.Google Scholar
Yannakakis, G. (2012). Game AI revisited. In Proceedings of ACM Computing Frontiers Conference.CrossRefGoogle Scholar
Yannakakis, G. N., & Hallam, J. (2009). Real-time game adaptation for optimizing player satisfaction. IEEE Transactions on Computational Intelligence and AI in Games, 1(2), 121133.10.1109/TCIAIG.2009.2024533CrossRefGoogle Scholar
Yannakakis, G. N., & Togelius, J. (2015). A panorama of artificial and computational intelligence in games. IEEE Transactions on Computational Intelligence and AI in Games, 7(4), 317335.10.1109/TCIAIG.2014.2339221CrossRefGoogle Scholar
Yee, N. (2014). The proteus paradox: How online games and virtual worlds change us – and how they don't. New Haven, CT: Yale University Press.Google Scholar
Yee, N., & Ducheneaut, N. (2015). The gamer motivation model in handy reference chart and slides. Retrieved from: http://quanticfoundry.com /2015/12/15/handy-reference/.Google Scholar
Zoeller, G. (2011). Game development telemetry. In Game Developers Conference.Google Scholar

References

Acar, Evrim, Dunlavy, Daniel M, & Kolda, Tamara G. (2009). Link prediction on evolving data using matrix and tensor factorizations. In Workshops at IEEE International Conference on Data Mining (pp. 262269).CrossRefGoogle Scholar
Adamic, Lada A, & Adar, Eytan. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211230.10.1016/S0378-8733(03)00009-1CrossRefGoogle Scholar
Al Hasan, Mohammad, & Zaki, Mohammed J. (2011). A survey of link prediction in social networks. In Social network data analytics (pp. 243275). New York: Science+Business Media.10.1007/978-1-4419-8462-3_9CrossRefGoogle Scholar
Albert, Réka, & Barabási, Albert-László. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.10.1103/RevModPhys.74.47CrossRefGoogle Scholar
Alvari, Hamidreza, Hajibagheri, Alireza, Sukthankar, Gita, & Lakkaraju, Kiran. (2016). Identifying community structures in dynamic networks. Social Network Analysis and Mining, 6(1), 77.10.1007/s13278-016-0390-5CrossRefGoogle Scholar
Backstrom, Lars, Huttenlocher, Dan, Kleinberg, Jon, & Lan, Xiangyang. (2006). Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4454).Google Scholar
Barabási, Albert-László, & Albert, Réka. (1999). Emergence of scaling in random networks. Science, 286(5439), 509512.10.1126/science.286.5439.509CrossRefGoogle ScholarPubMed
Barabási, Albert-László, et al. (2009). Scale-free networks: A decade and beyond. Science, 325(5939), 412.10.1126/science.1173299CrossRefGoogle ScholarPubMed
Benevenuto, Fabricio, Rodrigues, Tiago, Cha, Meeyoung, & Almeida, Virgílio. (2009). Characterizing user behavior in online social networks. In Proceedings of the ACM SIGCOMM Conference on Internet Measurement (pp. 4962).10.1145/1644893.1644900CrossRefGoogle Scholar
Bennerstedt, U., Ivarsson, J., & Linderoth, J. (2012). How gamers manage aggression: Situating skills in collaborative computer games. Computer-Supported Collaborative Learning, 7, 4361.10.1007/s11412-011-9136-6CrossRefGoogle Scholar
Berlingerio, Michele, Bonchi, Francesco, Bringmann, Björn, & Gionis, Aristides. (2009). Mining graph evolution rules. In Machine learning and knowledge discovery in databases (pp. 115130). New York, NY: Springer Science+Business Media.10.1007/978-3-642-04180-8_25CrossRefGoogle Scholar
Bianconi, Ginestra. (2013). Statistical mechanics of multiplex networks: Entropy and overlap. Physical Review E, 87(6), 062806.10.1103/PhysRevE.87.062806CrossRefGoogle ScholarPubMed
Blondel, Vincent D, Guillaume, Jean-Loup, Lambiotte, Renaud, & Lefebvre, Etienne. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.10.1088/1742-5468/2008/10/P10008CrossRefGoogle Scholar
Brin, Sergey, & Page, Lawrence. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer Networks, 56(18), 38253833.10.1016/j.comnet.2012.10.007CrossRefGoogle Scholar
Bringmann, Björn, Berlingerio, Michele, Bonchi, Francesco, & Gionis, Arisitdes. (2010). Learning and predicting the evolution of social networks. IEEE Intelligent Systems, 25(4), 2635.10.1109/MIS.2010.91CrossRefGoogle Scholar
Broder, Andrei, Kumar, Ravi, Maghoul, Farzin, et al. (2000). Graph structure in the web. Computer Networks, 33(1), 309320.10.1016/S1389-1286(00)00083-9CrossRefGoogle Scholar
Buldyrev, Sergey V, Parshani, Roni, Paul, Gerald, Stanley, H Eugene, & Havlin, Shlomo. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291), 10251028.10.1038/nature08932CrossRefGoogle ScholarPubMed
Buono, Camila, Alvarez-Zuzek, Lucila G, Macri, Pablo A, & Braunstein, Lidia A. (2014). Epidemics in partially overlapped multiplex networks. PloS ONE, 9(3), e92200.10.1371/journal.pone.0092200CrossRefGoogle ScholarPubMed
Cazabet, Rémy, Amblard, Frédéric, & Hanachi, Chihab. (2010). Detection of overlapping communities in dynamical social networks. In IEEE International Conference on Social Computing (pp. 309314).10.1109/SocialCom.2010.51CrossRefGoogle Scholar
Clauset, Aaron, Shalizi, Cosma Rohilla, & Newman, Mark EJ. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661703.10.1137/070710111CrossRefGoogle Scholar
Cook, Diane J, Crandall, Aaron, Singla, Geetika, & Thomas, Brian. (2010). Detection of social interaction in smart spaces. Cybernetics and Systems: An International Journal, 41(2), 90104.10.1080/01969720903584183CrossRefGoogle ScholarPubMed
Danon, Leon, Diaz-Guilera, Albert, Duch, Jordi, & Arenas, Alex. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.10.1088/1742-5468/2005/09/P09008CrossRefGoogle Scholar
Dawes, Robyn M. (1980). Social dilemmas. Annual Review of Psychology, 31(1), 169193.10.1146/annurev.ps.31.020180.001125CrossRefGoogle Scholar
De Domenico, Manlio, Solé-Ribalta, Albert, Cozzo, Emanuele, et al. (2013a). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022.10.1103/PhysRevX.3.041022CrossRefGoogle Scholar
De Domenico, Manlio, Sole, Albert, Gomez, Sergio, & Arenas, Alex. (2013b). Random walks on multiplex networks. arXiv preprint arXiv:1306.0519.Google Scholar
Ding, Ying. (2011). Applying weighted PageRank to author citation networks. Journal of the American Society for Information Science and Technology, 62(2), 236245.10.1002/asi.21452CrossRefGoogle Scholar
Faloutsos, Michalis, Faloutsos, Petros, & Faloutsos, Christos. (1999). On power-law relationships of the internet topology. In ACM SIGCOMM Computer Communication Review, Vol. 29 (pp. 251262).10.1145/316194.316229CrossRefGoogle Scholar
Fortunato, Santo. (2010). Community detection in graphs. Physics Reports, 486(3), 75174.10.1016/j.physrep.2009.11.002CrossRefGoogle Scholar
Getoor, Lise, & Diehl, Christopher P. (2005). Link mining: A survey. ACM SIGKDD Explorations Newsletter, 7(2), 312.10.1145/1117454.1117456CrossRefGoogle Scholar
Gomez, Sergio, Diaz-Guilera, Albert, Gomez-Gardenes, Jesus, Perez-Vicente, Conrad J, Moreno, Yamir, & Arenas, Alex. (2013). Diffusion dynamics on multiplex networks. Physical Review Letters, 110(2), 028701.10.1103/PhysRevLett.110.028701CrossRefGoogle ScholarPubMed
Gómez-Gardenes, Jesús, Reinares, Irene, Arenas, Alex, & Floría, Luis Mario. (2012). Evolution of cooperation in multiplex networks. Scientific Reports, 2.10.1038/srep00620CrossRefGoogle ScholarPubMed
Hajibagheri, Alireza, Sukthankar, Gita, & Lakkaraju, Kiran. (2016). Leveraging network dynamics for improved link prediction. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction.10.1007/978-3-319-39931-7_14CrossRefGoogle Scholar
Hristova, Desislava, Noulas, Anastasios, Brown, Chloë, Musolesi, Mirco, & Mascolo, Cecilia. (2015). A multilayer approach to multiplexity and link prediction in online geo-social networks. arXiv preprint arXiv:1508.07876.Google Scholar
Huang, Zan, & Lin, Dennis K. J. (2009). The time-series link prediction problem with applications in communication surveillance. INFORMS Journal on Computing, 21(2), 286303.10.1287/ijoc.1080.0292CrossRefGoogle Scholar
Humphreys, M., & Weinstein, J. (2008). Who fights? The determinants of participation in civil war. American Journal of Political Science, 52(2), 436455.10.1111/j.1540-5907.2008.00322.xCrossRefGoogle Scholar
Keegan, B., Ahmed, M., Williams, D., Srivastava, J., & Contractor, N. (2010). Dark Gold: Statistical properties of clandestine networks in massively multiplayer online games. In IEEE International Conference on Social Computing (pp. 201208).10.1109/SocialCom.2010.36CrossRefGoogle Scholar
Kivela, Mikko, Arenas, Alex, Barthelemy, Marc, Gleeson, James, Moreno, Yamir, & Porter, Mason. (2014). Multilayer networks. Journal of Complex Networks, 2, 203271.10.1093/comnet/cnu016CrossRefGoogle Scholar
Korsgaard, M., Picot, A., Wigand, Rolf, Welpe, I., & Assmann, J. (2010). Cooperation, coordination, and trust in virtual teams: Insights from virtual games. In Online worlds: Convergence of the real and the virtual (pp. 253--264). New York, NY: Springer Science+Business Media.Google Scholar
Kurant, Maciej, & Thiran, Patrick. (2006). Layered complex networks. Physical Review Letters, 96(13), 138701.10.1103/PhysRevLett.96.138701CrossRefGoogle ScholarPubMed
Lancichinetti, Andrea, Radicchi, Filippo, Ramasco, José J, et al. (2011). Finding statistically significant communities in networks. PloS One, 6(4), e18961.10.1371/journal.pone.0018961CrossRefGoogle ScholarPubMed
Leskovec, Jure, Backstrom, Lars, Kumar, Ravi, & Tomkins, Andrew. (2008). Microscopic evolution of social networks. In Proceedings ofthe ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 462470).10.1145/1401890.1401948CrossRefGoogle Scholar
Liben-Nowell, David, & Kleinberg, Jon. (2003). The Link Prediction Problem for Social Networks. In Proceedings of the International Conference on Information and Knowledge Management (pp. 556559).10.1145/956863.956972CrossRefGoogle Scholar
Liben-Nowell, David, & Kleinberg, Jon. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 10191031.10.1002/asi.20591CrossRefGoogle Scholar
Lichtenwalter, Ryan N., Lussier, Jake T., & Chawla, Nitesh V. (2010). New perspectives and methods in link prediction. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 243252).10.1145/1835804.1835837CrossRefGoogle Scholar
Liu, Yu-Ting, Liu, Tie-Yan, Qin, Tao, Ma, Zhi-Ming, & Li, Hang. (2007). Supervised rank aggregation. In Proceedings of the International Conference on World Wide Web (pp. 481490).10.1145/1242572.1242638CrossRefGoogle Scholar
MacKay, David JC. (2003). Information theory, inference and learning algorithms. Cambridge: Cambridge University Press.Google Scholar
Min, Byungjoon, & Goh, K-I. (2013). Layer-crossing overhead and information spreading in multiplex social networks. arXiv preprint arXiv:1307.2967.Google Scholar
Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks. Physical Review E, 64, 025102.10.1103/PhysRevE.64.025102CrossRefGoogle Scholar
Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.10.1103/PhysRevLett.89.208701CrossRefGoogle ScholarPubMed
Nicosia, Vincenzo, Bianconi, Ginestra, Latora, Vito, & Barthelemy, Marc. (2013). Growing multiplex networks. Physical Review Letters, 111(5), 058701.10.1103/PhysRevLett.111.058701CrossRefGoogle ScholarPubMed
Piraveenan, Mahendra, Chung, Kon Shing Kenneth, & Uddin, Shahadat. (2012). Assortativity of links in directed networks. In Foundations of Computer Science Conference. Retrieved from: www.academia.edu/1892630/Assortativity_of_links_in_directed_networks.Google Scholar
Potgieter, Anet, April, Kurt A, Cooke, Richard JE, & Osunmakinde, Isaac O. (2009). Temporality in link prediction: Understanding social complexity. Emergence: Complexity & Organization (E: CO), 11(1), 6983.Google Scholar
Pujari, Manisha, & Kanawati, Rushed. (2012). Supervised rank aggregation approach for link prediction in complex networks. Proceedings of the International World Wide Web Conference (pp. 11891196).10.1145/2187980.2188260CrossRefGoogle Scholar
Pujari, Manisha, & Kanawati, Rushed. (2015). Link prediction in multiplex networks. Networks and Heterogeneous Media, 10(1), 1735.10.3934/nhm.2015.10.17CrossRefGoogle Scholar
Rosvall, Martin, & Bergstrom, Carl T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences of the USA, 105(4), 11181123.10.1073/pnas.0706851105CrossRefGoogle ScholarPubMed
Roy, A., Borbora, Z., & Srivastava, J. (2013). Socialization and Trust Formation: A Mutual Reinforcement? An Exploratory Analysis in an Online Virtual Setting. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 653660).10.1145/2492517.2492550CrossRefGoogle Scholar
Saumell-Mendiola, Anna, Serrano, M Ángeles, & Boguná, Marián. (2012). Epidemic spreading on interconnected networks. Physical Review E, 86(2), 026106.10.1103/PhysRevE.86.026106CrossRefGoogle ScholarPubMed
Scott, John. (2012). Social Network Analysis. SAGE.Google Scholar
Sculley, D. (2007). Rank Aggregation for Similar Items. In SIAM International Conference on Data Mining (pp. 587592).10.1137/1.9781611972771.66CrossRefGoogle Scholar
Snijders, T., van de Bunt, G., & Steglich, C. E. G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 4460.10.1016/j.socnet.2009.02.004CrossRefGoogle Scholar
Soares, Paulo Ricardo da Silva, & Prudêncio, Ricardo Bastos Cavalcante. (2012). Time series based link prediction. In International Joint Conference on Neural Networks (pp. 17). IEEE.Google Scholar
Sole-Ribalta, Albert, De Domenico, Manlio, Kouvaris, Nikos E, Diaz-Guilera, Albert, Gomez, Sergio, & Arenas, Alex. (2013). Spectral properties of the Laplacian of multiplex networks. Physical Review E, 88(3), 032807.10.1103/PhysRevE.88.032807CrossRefGoogle ScholarPubMed
Strogatz, Steven H. (2001). Exploring complex networks. Nature, 410(6825), 268276.10.1038/35065725CrossRefGoogle ScholarPubMed
Tabourier, Lionel, Bernardes, Daniel Faria, Libert, Anne-Sophie, & Lambiotte, Renaud. (2014). RankMerging: A supervised learning-to-rank framework to predict links in large social network. arXiv preprint arXiv:1407.2515.Google Scholar
Tan, Pang-Ning, Steinbach, Michael, & Kumar, Vipin. (2005). Introduction to data mining, 1st edn. Boston, MA: Addison-Wesley Longman.Google Scholar
Thurau, C., & Bauckhage, C. (2010). Analyzing the evolution of social groups in World of Warcraft. In IEEE International Conference on Computational Intelligence in Games (pp. 170177).10.1109/ITW.2010.5593358CrossRefGoogle Scholar
Wang, Chao, Satuluri, Venu, & Parthasarathy, Srinivasan. (2007). Local probabilistic models for link prediction. In Seventh IEEE International Conference on Data Mining (pp. 322331).10.1109/ICDM.2007.108CrossRefGoogle Scholar
Wigand, R., Agrawal, N., Osesina, O., Hering, W., Korsgaard, M., Picot, A., & Drescher, M. (2012). Social network indices as performance predictors in a virtual organization. In International Conference on Computational Analysis of Social Networks (pp. 144149). Retrieved from: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6396507.Google Scholar
Xie, Jierui, Chen, Mingming, & Szymanski, Boleslaw K. (2013). LabelrankT: Incremental community detection in dynamic networks via label propagation. arXiv preprint arXiv:1305.2006.Google Scholar
Yee, N. (2006). The labor of fun: How video games blur the boundaries of work and play. Games and Culture, 1(1), 6871.10.1177/1555412005281819CrossRefGoogle Scholar
Zhou, Tao, , Linyuan, & Zhang, Yi-Cheng. (2009). Predicting missing links via local information. The European Physical Journal B, 71(4), 623630.10.1140/epjb/e2009-00335-8CrossRefGoogle Scholar

Accessibility standard: Unknown

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×