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14 - Using Massively Multiplayer Online Game Data to Analyze the Dynamics of Social Interactions

from Part IV - Techniques for Analyzing Game Data

Published online by Cambridge University Press:  15 June 2018

By
Alireza Hajibagheri ,
Gita Sukthankar ,
Kiran Lakkaraju ,
Hamidreza Alvari ,
Rolf T. Wigand  and
Nitin Agarwal
Edited by
Kiran Lakkaraju ,
Gita Sukthankar  and
Rolf T. Wigand
Kiran Lakkaraju
Affiliation:
Sandia National Laboratories, New Mexico
Gita Sukthankar
Affiliation:
University of Central Florida
Rolf T. Wigand
Affiliation:
University of Arkansas
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Social Interactions in Virtual Worlds
An Interdisciplinary Perspective
 , pp. 375 - 416
Publisher: Cambridge University Press
Print publication year: 2018

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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. 262–269).CrossRefGoogle Scholar
Adamic, Lada A, & Adar, Eytan. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.CrossRefGoogle Scholar
Al Hasan, Mohammad, & Zaki, Mohammed J. (2011). A survey of link prediction in social networks. In Social network data analytics (pp. 243–275). New York: Science+Business Media.CrossRefGoogle Scholar
Albert, Réka, & Barabási, Albert-László. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.CrossRefGoogle 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. 44–54).CrossRefGoogle Scholar
Barabási, Albert-László, & Albert, Réka. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.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. 49–62).Google Scholar
Bennerstedt, U., Ivarsson, J., & Linderoth, J. (2012). How gamers manage aggression: Situating skills in collaborative computer games. Computer-Supported Collaborative Learning, 7, 43–61.CrossRefGoogle Scholar
Berlingerio, Michele, Bonchi, Francesco, Bringmann, Björn, & Gionis, Aristides. (2009). Mining graph evolution rules. In Machine learning and knowledge discovery in databases (pp. 115–130). New York, NY: Springer Science+Business Media.CrossRefGoogle 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.CrossRefGoogle Scholar
Brin, Sergey, & Page, Lawrence. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer Networks, 56(18), 3825–3833.CrossRefGoogle Scholar
Bringmann, Björn, Berlingerio, Michele, Bonchi, Francesco, & Gionis, Arisitdes. (2010). Learning and predicting the evolution of social networks. IEEE Intelligent Systems, 25(4), 26–35.CrossRefGoogle Scholar
Broder, Andrei, Kumar, Ravi, Maghoul, Farzin, et al. (2000). Graph structure in the web. Computer Networks, 33(1), 309–320.CrossRefGoogle Scholar
Buldyrev, Sergey V, Parshani, Roni, Paul, Gerald, Stanley, H Eugene, & Havlin, Shlomo. (2010). Catastrophic cascade of failures in interdependent networks. Nature, 464(7291), 1025–1028.CrossRefGoogle 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.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. 309–314).Google Scholar
Clauset, Aaron, Shalizi, Cosma Rohilla, & Newman, Mark EJ. (2009). Power-law distributions in empirical data. SIAM Review, 51(4), 661–703.CrossRefGoogle 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), 90–104.CrossRefGoogle 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.CrossRefGoogle Scholar
Dawes, Robyn M. (1980). Social dilemmas. Annual Review of Psychology, 31(1), 169–193.CrossRefGoogle 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), 236–245.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. 251–262).Google Scholar
Fortunato, Santo. (2010). Community detection in graphs. Physics Reports, 486(3), 75–174.CrossRefGoogle Scholar
Getoor, Lise, & Diehl, Christopher P. (2005). Link mining: A survey. ACM SIGKDD Explorations Newsletter, 7(2), 3–12.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), 286–303.Google Scholar
Humphreys, M., & Weinstein, J. (2008). Who fights? The determinants of participation in civil war. American Journal of Political Science, 52(2), 436–455.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. 201–208).CrossRefGoogle Scholar
Kivela, Mikko, Arenas, Alex, Barthelemy, Marc, Gleeson, James, Moreno, Yamir, & Porter, Mason. (2014). Multilayer networks. Journal of Complex Networks, 2, 203–271.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
Lancichinetti, Andrea, Radicchi, Filippo, Ramasco, José J, et al. (2011). Finding statistically significant communities in networks. PloS One, 6(4), e18961.CrossRefGoogle 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. 462–470).CrossRefGoogle 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. 556–559).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), 1019–1031.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. 243–252).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. 481–490).CrossRefGoogle 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), 69–83.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. 1189–1196).Google Scholar
Pujari, Manisha, & Kanawati, Rushed. (2015). Link prediction in multiplex networks. Networks and Heterogeneous Media, 10(1), 17–35.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), 1118–1123.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. 653–660).CrossRefGoogle 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. 587–592).Google Scholar
Snijders, T., van de Bunt, G., & Steglich, C. E. G. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44–60.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. 1–7). 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.CrossRefGoogle ScholarPubMed
Strogatz, Steven H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.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. 170–177).CrossRefGoogle Scholar
Wang, Chao, Satuluri, Venu, & Parthasarathy, Srinivasan. (2007). Local probabilistic models for link prediction. In Seventh IEEE International Conference on Data Mining (pp. 322–331).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. 144–149). 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), 68–71.Google Scholar
Zhou, Tao, Lü, Linyuan, & Zhang, Yi-Cheng. (2009). Predicting missing links via local information. The European Physical Journal B, 71(4), 623–630.Google Scholar

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