Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-04T21:43:42.609Z 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.'
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.)

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.Google 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), 11:1–11:29Google 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.Google 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.Google 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.Google 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.Google 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.Google 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.Google 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.Google Scholar
Bohannon, J. (2010). Game-miners grapple with massive data. Science, 330(6000), 3031.Google Scholar
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.Google 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.Google Scholar
Drachen, A., & Canossa, A. (2011). Evaluating motion: Spatial user behaviour in virtual environments. International Journal of Arts and Technology, 4(2), 294314.Google 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.Google 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.Google 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.Google 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.Google 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.Google 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.Google 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.Google 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.Google 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.Google Scholar
Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4(1), 7790.Google 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.Google 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.Google 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.Google Scholar
Sifa, R., & Bauckhage, C. (2013). Archetypical motion: Supervised game behavior learning with archetypal analysis. In Proceedings of IEEE CIG.Google Scholar
Sifa, R., Drachen, A., Bauckhage, C., Thurau, C., & Canossa, A. (2013). Behavior evolution in Tomb Raider underworld. In Proceedings of IEEE CIG.Google 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.Google 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.Google 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.Google 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.Google Scholar
Tastan, B., Chang, Y., & Sukthankar, G. (2012). Learning to intercept opponents in first person shooter games. In Proceedings of IEEE CIG.Google 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.Google 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.Google Scholar
Thawonmas, R., Yoshida, K., Lou, J.-K., & Chen, K.-T. (2011). Analysis of revisitations in online games. Entertainment Computing, 2(4), 215221.Google 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.Google 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.Google 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.Google Scholar
van Lankveld, G., Spronck, P., Van Den Herik, J., & Arntz, A. (2011). Games as personality profiling tools. In Proceedings of IEEE CIG.Google Scholar
Weber, B., & Mateas, M. (2009). A data mining approach to strategy prediction. In Proceedings of IEEE CIG.Google Scholar
Weber, B. G., John, M., Mateas, M., & Jhala, A. (2011). Modeling player retention in Madden NFL 11. In Proceedings of IAAI.Google 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.Google 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.Google 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.Google 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.Google 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).Google Scholar
Adamic, Lada A, & Adar, Eytan. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211230.Google 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.Google Scholar
Albert, Réka, & Barabási, Albert-László. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.Google Scholar
Alvari, Hamidreza, Hajibagheri, Alireza, Sukthankar, Gita, & Lakkaraju, Kiran. (2016). Identifying community structures in dynamic networks. Social Network Analysis and Mining, 6(1), 77.Google 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.Google Scholar
Barabási, Albert-László, et al. (2009). Scale-free networks: A decade and beyond. Science, 325(5939), 412.Google Scholar
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).Google Scholar
Bennerstedt, U., Ivarsson, J., & Linderoth, J. (2012). How gamers manage aggression: Situating skills in collaborative computer games. Computer-Supported Collaborative Learning, 7, 4361.Google 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.Google Scholar
Bianconi, Ginestra. (2013). Statistical mechanics of multiplex networks: Entropy and overlap. Physical Review E, 87(6), 062806.Google Scholar
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.Google Scholar
Brin, Sergey, & Page, Lawrence. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer Networks, 56(18), 38253833.Google Scholar
Bringmann, Björn, Berlingerio, Michele, Bonchi, Francesco, & Gionis, Arisitdes. (2010). Learning and predicting the evolution of social networks. IEEE Intelligent Systems, 25(4), 2635.Google Scholar
Broder, Andrei, Kumar, Ravi, Maghoul, Farzin, et al. (2000). Graph structure in the web. Computer Networks, 33(1), 309320.Google 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.Google Scholar
Buono, Camila, Alvarez-Zuzek, Lucila G, Macri, Pablo A, & Braunstein, Lidia A. (2014). Epidemics in partially overlapped multiplex networks. PloS ONE, 9(3), e92200.Google Scholar
Cazabet, Rémy, Amblard, Frédéric, & Hanachi, Chihab. (2010). Detection of overlapping communities in dynamical social networks. In IEEE International Conference on Social Computing (pp. 309314).Google Scholar
Clauset, Aaron, Shalizi, Cosma Rohilla, & Newman, Mark EJ. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661703.Google 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.Google Scholar
Danon, Leon, Diaz-Guilera, Albert, Duch, Jordi, & Arenas, Alex. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.Google Scholar
Dawes, Robyn M. (1980). Social dilemmas. Annual Review of Psychology, 31(1), 169193.Google Scholar
De Domenico, Manlio, Solé-Ribalta, Albert, Cozzo, Emanuele, et al. (2013a). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022.Google 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.Google 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).Google Scholar
Fortunato, Santo. (2010). Community detection in graphs. Physics Reports, 486(3), 75174.Google Scholar
Getoor, Lise, & Diehl, Christopher P. (2005). Link mining: A survey. ACM SIGKDD Explorations Newsletter, 7(2), 312.Google 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.Google Scholar
Gómez-Gardenes, Jesús, Reinares, Irene, Arenas, Alex, & Floría, Luis Mario. (2012). Evolution of cooperation in multiplex networks. Scientific Reports, 2.Google Scholar
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.Google 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.Google Scholar
Humphreys, M., & Weinstein, J. (2008). Who fights? The determinants of participation in civil war. American Journal of Political Science, 52(2), 436455.Google 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).Google Scholar
Kivela, Mikko, Arenas, Alex, Barthelemy, Marc, Gleeson, James, Moreno, Yamir, & Porter, Mason. (2014). Multilayer networks. Journal of Complex Networks, 2, 203271.Google 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.Google Scholar
Lancichinetti, Andrea, Radicchi, Filippo, Ramasco, José J, et al. (2011). Finding statistically significant communities in networks. PloS One, 6(4), e18961.Google Scholar
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).Google 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).Google 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.Google 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).Google 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).Google 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.Google Scholar
Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.Google Scholar
Nicosia, Vincenzo, Bianconi, Ginestra, Latora, Vito, & Barthelemy, Marc. (2013). Growing multiplex networks. Physical Review Letters, 111(5), 058701.Google Scholar
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).Google Scholar
Pujari, Manisha, & Kanawati, Rushed. (2015). Link prediction in multiplex networks. Networks and Heterogeneous Media, 10(1), 1735.Google 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.Google Scholar
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).Google Scholar
Saumell-Mendiola, Anna, Serrano, M Ángeles, & Boguná, Marián. (2012). Epidemic spreading on interconnected networks. Physical Review E, 86(2), 026106.Google Scholar
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).Google Scholar
Snijders, T., van de Bunt, G., & Steglich, C. E. G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 4460.Google 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.Google Scholar
Strogatz, Steven H. (2001). Exploring complex networks. Nature, 410(6825), 268276.Google Scholar
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).Google Scholar
Wang, Chao, Satuluri, Venu, & Parthasarathy, Srinivasan. (2007). Local probabilistic models for link prediction. In Seventh IEEE International Conference on Data Mining (pp. 322331).Google 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.Google Scholar
Zhou, Tao, , Linyuan, & Zhang, Yi-Cheng. (2009). Predicting missing links via local information. The European Physical Journal B, 71(4), 623630.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] 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
×