Alsén A, Runge J, Drachen A, Klapper D. (2016) Play with me? understanding and measuring the social aspect of casual gaming. In: twelfth artificial intelligence and interactive digital entertainment conference
Backiel A, Verbinnen Y, Baesens B, Claeskens G. (2015) Combining local and social network classifiers to improve churn prediction. In: 2015 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp. 651–658 . IEEE
Baesens B (2014) Analytics in a big data world: the essential guide to data science and its applications. John Wiley, USA
Google Scholar
Borbora Z.H, Srivastava J. (2012) User behavior modelling approach for churn prediction in online games. In: 2012 international conference on privacy, security, risk and trust and 2012 international confernece on social computing, pp 51–60 . IEEE
Borbora Z, Chandra A, Kumaraguru P, Srivastava J.(2019) On churn and social contagion. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp 833–841
van de Bovenkamp R, Shen S, Jia AL, Kuipers F et al (2014) Analyzing implicit social networks in multiplayer online games. IEEE Internet Comput 18(3):36–44
Article
Google Scholar
Calzada-Infante L, Óskarsdóttir M, Baesens B (2020) Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts. Expert Syst Appl 160:113553
Article
Google Scholar
Canossa A, Azadvar A, Harteveld C, Drachen A, Deterding S (2019) Influencers in multiplayer online shooters: Evidence of social contagion in playtime and social play. In: proceedings of the 2019 CHI conference on human factors in computing systems, pp 1–12
De Caigny A, Coussement K, De Bock KW (2018) A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur J Oper Res 269(2):760–772
Article
MathSciNet
MATH
Google Scholar
Drachen A, Pastor M, Liu A, Fontaine D.J, Chang Y, Runge J, Sifa R, Klabjan D (2018) To be or not to be... social: Incorporating simple social features in mobile game customer lifetime value predictions. In: proceedings of the australasian computer science week multiconference, pp. 1–10
Fu X, Chen X, Shi Y-T, Bose I, Cai S (2017) User segmentation for retention management in online social games. Decis Support Syst 101:51–68
Article
Google Scholar
Getoor L. (2005) Link-based classification. In: advanced methods for knowledge discovery from complex data, pp 189–207. Springer, USA
Gu L., Jia A.L. (2018) Player activity and popularity in online social games and their implications for player retention. In: 2018 16th annual workshop on network and systems support for games (NetGames), pp 1–6 . IEEE
Géron A. (2019) Hands-on Machine Learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent systems. “O’Reilly Media, Inc.”, ???
Hadiji F, Sifa R, Drachen A, Thurau C, Kersting K, Bauckhage C. (2014) Predicting player churn in the wild. In: 2014 IEEE conference on computational intelligence and games pp 1–8 . Ieee
Henckaerts R, Côté M-P, Antonio K, Verbelen R (2021) Boosting insights in insurance tariff plans with tree-based machine learning methods. North Am Actuarial J 25(2):255–285
Article
MathSciNet
MATH
Google Scholar
Jeon J, Yoon D, Yang S, Kim K. (2017) Extracting gamers’ cognitive psychological features and improving performance of churn prediction from mobile games. In: 2017 IEEE conference on computational intelligence and games (CIG), pp 150–153 . IEEE
Jia AL, Shen S, Bovenkamp RVD, Iosup A, Kuipers F, Epema DH (2015) Socializing by gaming: Revealing social relationships in multiplayer online games. ACM Trans Knowl Discov Data (TKDD) 10(2):1–29
Article
Google Scholar
Karsai M, Kivelä M, Pan RK, Kaski K, Kertész J, Barabási A-L, Saramäki J (2011) Small but slow world: how network topology and burstiness slow down spreading. Phys Rev 83(2):025102
Google Scholar
Kilimci Z.H, Yörük H, Akyokus S.(2020) Sentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithms. In: 2020 international conference on innovations in intelligent systems and applications (INISTA), pp 1–7 . IEEE
Kim S, Choi D, Lee E, Rhee W (2017) Churn prediction of mobile and online casual games using play log data. PloS one 12(7):0180735
Google Scholar
Kitola M. (2019) Impact of social features on player retention in free-to-play mobile games
Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Net 2(3):203–271
Article
Google Scholar
Klauser M, Nacke L.E, Prescod P.(2013) Social player analytics in a facebook health game. In: proceedings of the CHI 2013 workshop on designing and evaluating sociability in online video games pp 39–44
Liu D-R, Liao H-Y, Chen K-Y, Chiu Y-L (2019) Churn prediction and social neighbour influences for different types of user groups in virtual worlds. Expert Syst 36(3):12384
Article
Google Scholar
Liu X, Xie M, Wen X, Chen R, Ge Y, Duffield N, Wang N (2020) Micro-and macro-level churn analysis of large-scale mobile games. Knowl Inf Syst 62(4):1465–1496
Article
Google Scholar
Liu X, Xie M, Wen X, Chen R, Ge Y, Duffield N, Wang N. (2018) A semi-supervised and inductive embedding model for churn prediction of large-scale mobile games. In: 2018 IEEE international conference on data mining (ICDM), pp 277–286. IEEE
Loria E, Marconi A (2021) Exploiting limited players’ behavioral data to predict churn in gamification. Electronic Commerce Res Appl 47:101057
Article
Google Scholar
Loria E, Nacke L.E, Pirker J. (2021) The quirks of being a wallflower: Towards defining lurkers and loners in games through a systematic literature review. In: extended abstracts of the 2021 CHI conference on human factors in computing systems pp 1–7
Óskarsdóttir M, Bravo C, Sarraute C, Vanthienen J, Baesens B (2019) The value of big data for credit scoring: enhancing financial inclusion using mobile phone data and social network analytics. Appl Soft Comput 74:26–39
Article
Google Scholar
Óskarsdóttir M, Bravo C, Verbeke W, Sarraute C, Baesens B, Vanthienen J.(2016) A comparative study of social network classifiers for predicting churn in the telecommunication industry. In: 2016 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM), pp 1151–1158 . IEEE
Óskarsdóttir M, Bravo C, Verbeke W, Sarraute C, Baesens B, Vanthienen J (2017) Social network analytics for churn prediction in telco: model building, evaluation and network architecture. Expert Syst Appl 85:204–220
Article
Google Scholar
Óskarsdóttir M, Van Calster T, Baesens B, Lemahieu W, Vanthienen J (2018) Time series for early churn detection: using similarity based classification for dynamic networks. Expert Syst Appl 106:55–65
Article
Google Scholar
Óskarsdóttir M, Ahmed W, Antonio K, Baesens B, Dendievel R, Donas T, Reynkens T.(2021) Social network analytics for supervised fraud detection in insurance. Risk Anal n/a(n/a) https://doi.org/10.1111/risa.13693. https://onlinelibrary.wiley.com/doi/pdf/10.1111/risa.13693
Óskarsdóttir M, Sarraute C, Bravo C, Baesens B, Vanthienen J.(2018) Credit scoring for good: Enhancing financial inclusion with smartphone-based microlending. In: international conference on information systems 2018, ICIS 2018, pp 1–9 . Association for information systems
Periáñez Á, Saas A, Guitart A, Magne C. (2016) Churn prediction in mobile social games: Towards a complete assessment using survival ensembles. In: 2016 IEEE international conference on data science and advanced analytics (DSAA), pp 564–573 . IEEE
Petersen F.W, Thomsen L.E, Mirza-Babaei P, Drachen A. (2017) Evaluating the onboarding phase of free-toplay mobile games: A mixed-method approach. In: proceedings of the annual symposium on computer-human interaction in play pp 377–388
Pirker J, Rattinger A, Drachen A, Sifa R (2018) Analyzing player networks in destiny. Entertain Comput 25:71–83
Article
Google Scholar
Rattinger A, Wallner G, Drachen A, Pirker J, Sifa R.(2016) Integrating and inspecting combined behavioral profiling and social network models in destiny. In: international conference on entertainment computing pp 77–89 . Springer
Rothmeier K, Pflanzl N, Hüllmann JA, Preuss M (2020) Prediction of player churn and disengagement based on user activity data of a freemium online strategy game. IEEE Trans Games 13(1):78–88
Article
Google Scholar
Runge J, Gao P, Garcin F, Faltings B. (2014) Churn prediction for high-value players in casual social games. In: 2014 IEEE conference on computational intelligence and games, pp 1–8. IEEE
Schiller MH, Wallner G, Schinnerl C, Calvo AM, Pirker J, Sifa R, Drachen A (2018) Inside the group: investigating social structures in player groups and their influence on activity. IEEE Trans Games 11(4):416–425
Article
Google Scholar
Schlauch W.E, Zweig K.A. (2015) Social network analysis and gaming: survey of the current state of art. In: joint international conference on serious games, pp 158–169. Springer
Seufert EB (2013) Freemium economics: leveraging analytics and user segmentation to drive revenue. Elsevier, USA
Google Scholar
Sifa R, Hadiji F, Runge J, Drachen A, Kersting K, Bauckhage C. (2015) Predicting purchase decisions in mobile free-to-play games. In: proceedings of the AAAI conference on artificial intelligence and interactive digital entertainment, vol 11, pp 79–85
Sorvari T (2018) How the different retention and monetization features affect the user experience in free-to-play mobile games (Unpublished master’s thesis). Laurea-ammattikorkeakoulu, Finland
Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B (2012) New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur J Oper Res 218(1):211–229
Article
Google Scholar
Verbeke W, Martens D, Baesens B (2014) Social network analysis for customer churn prediction. Appl Soft Comput 14:431–446
Article
Google Scholar
Vilar De Carvalho Santos G. (2020) Feature importance analysis for user lifetime value prediction in games using machine learning: an exploratory approach. PhD thesis, Politecnico di Torino
Wallner G, Schinnerl C, Schiller MH, Calvo AM, Pirker J, Sifa R, Drachen A (2019) Beyond the individual: understanding social structures of an online player matchmaking website. Entertain Comput 30:100284
Article
Google Scholar
Wei P-S, Lu H-P (2014) Why do people play mobile social games? an examination of network externalities and of uses and gratifications. Internet Res 24(3):313–331
Wu Z, Pan S, Chen F, Long G, Zhang C, Philip S.Y. (2020) A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems
Zeng Y, Sapienza A, Ferrara E. (2019) The influence of social ties on performance in team-based online games. IEEE Transactions on Games
Zhu J, Yan Y, Zhao L, Heimann M, Akoglu L, Koutra D (2020) Beyond homophily in graph neural networks: current limitations and effective designs. Adv Neural Inf Process Syst 33:7793–7804
Google Scholar