Abstract
While direct social ties have been intensely studied in the context of computer-mediated social networks, indirect ties (e.g., friends of friends) have seen less attention. Yet in real life, we often rely on friends of our friends for recommendations (of doctors, schools, or babysitters), for introduction to a new job opportunity, and for many other occasional needs. In this work we empirically study the predictive power of indirect ties in two dynamic processes in social networks: new link formation and information diffusion. We not only verify the predictive power of indirect ties in new link formation but also show that this power is effective over longer social distance. Moreover, we show that the strength of an indirect tie positively correlates to the speed of forming a new link between the two end users of the indirect tie. Finally, we show that the strength of indirect ties can serve as a predictor for diffusion paths in social networks.
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References
Li, Z., Shen, H.: Soap: A social network aided personalized and effective spam filter to clean your e-mail box. In: Proceedings IEEE INFOCOM (2011)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: AAAI/IAAI, pp. 714–720 (1998)
Li, J., Dabek, F.: F2F: reliable storage in open networks. In: Proceedings of the 4th International Workshop on Peer-to-Peer Systems, IPTPS (2006)
Kahanda, I., Neville, J.: Using transactional information to predict link strength in online social networks. In: ICWSM (2009)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 211–220. ACM (2009)
Granovetter, M.S.: A study of contacts and careers. Cambridge, Mass. Harvard (1974)
McPherson, M., Smith-Lovin, L., Cook, J.: Birds of a feather: Homophily in social networks. Annual review of sociology, 415–444 (2001)
Granovetter, M.S.: The strength of weak ties. American Journal of Sociology 78(6) (1973)
Coleman, J.: Social capital in the creation of human capital. American Journal of Sociology, S95–S120 (1988)
Rapoport, A.: Spread of information through a population with socio-structural bias: I. assumption of transitivity. The bulletin of mathematical biophysics 15(4), 523–533 (1953)
Szell, M., Thurner, S.: Measuring social dynamics in a massive multiplayer online game. Social Networks 32(4), 313–329 (2010)
Kossinets, G., Watts, D.J.: Origins of homophily in an evolving social network1. American Journal of Sociology 115(2), 405–450 (2009)
Kleinbaum, A.M.: Organizational misfits and the origins of brokerage in intrafirm networks. Administrative Science Quarterly 57(3), 407–452 (2012)
Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: memberships, growth, and evolution. In: The International Conference on Knowledge Discovery and Data Mining, KDD (2006)
Patil, A., Liu, J., Gao, J.: Predicting group stability in online social networks. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1021–1030 (2013)
Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in twitter. In: ICWSM, vol. 10, pp. 355–358 (2010)
Blackburn, J., Iamnitchi, A.: Relationships under the microscope with interaction-backed social networks. In: 1st International Conference on Internet Science, Brussels, Belgium (2013)
Blackburn, J., Kourtellis, N., Skvoretz, J., Ripeanu, M., Iamnitchi, A.: Cheating in online games: A social network perspective. ACM Transactions on Internet Technology (TOIT) 13(3), 9 (2014)
Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.-F., den Broeck, W.V.: What’s in a crowd? Analysis of face-to-face behavioral networks. Journal of Theoretical Biology 271(1), 166–180 (2011)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: International Conference on Knowledge Discovery and Data Mining, KDD (2009)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American society for information science and technology 58(7), 1019–1031 (2007)
Lü, L., Zhou, T.: Physica A: Statistical Mechanics and its Applications. Physica A: Statistical Mechanics and its Applications 390(6), 1150–1170 (2011)
Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: 19th International Conference on World Wide Web, Raleigh, NC, USA, pp. 981–990 (2010)
Friedkin, N.E.: Horizons of observability and limits of informal control in organizations. Social Forces 62(6), 54–77 (1983)
Christakis, N.A., Fowler, J.H.: Connected: The surprising power of our social networks and how they shape our lives. Hachette Digital, Inc. (2009)
Adamic, L., Adar, E.: Friends and neighbors on the web. Social Networks 25(3), 211–230 (2003)
Kourtellis, N.: On the design of socially-aware distributed systems. Ph.D. dissertation, University of South Florida (2012)
Zuo, X., Blackburn, J., Kourtellis, N., Skvoretz, J., Iamnitchi, A.: The power of indirect ties in friend-to-friend storage systems. In: 14th IEEE International Conference on Peer-to-Peer Computing (September 2014)
Kubat, M., Matwin, S., et al.: Addressing the curse of imbalanced training sets: one-sided selection. ICML 97, 179–186 (1997)
Chawla, N.V., Bowyer, K.W., Hall, H.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Zignani, M., Gaito, S., Rossi, G.P., Zhao, X., Zheng, H., Zhao, B.Y.: Link and triadic closure delay: Temporal metrics for social network dynamics. In: ICWSM 2014 (2014)
Yildiz, M.E., Scaglione, A., Ozdaglar, A.: Asymmetric information diffusion via gossiping on static and dynamic networks. In: 49th IEEE Conference on Decision and Control (CDC), pp. 7467–7472 (December 2010)
Guille, A., Hacid, H.: A predictive model for the temporal dynamics of information diffusion in online social networks. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 1145–1152 (2012)
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012, pp. 519–528 (2012)
Weng, L., Ratkiewicz, J., Perra, N., Gonçalves, B., Castillo, C., Bonchi, F., Schifanella, R., Menczer, F., Flammini, A.: The role of information diffusion in the evolution of social networks. In: Proceedings of the 19th ACM International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 356–364 (2013)
Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: A survey. SIGMOD Rec. 42(2), 17–28 (2013)
Fowler, J.H., Christakis, N.A., Roux, D.: Dynamic spread of happiness in a large social network: longitudinal analysis of the framingham heart study social network. BMJ: British Medical Journal, 23–27 (2009)
Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357(4), 370–379 (2007)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 137–146. ACM, New York (2003)
Newman, M.E.: The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences 98(2), 404–409 (2001)
Wei, X., Yang, J., Adamic, L.A., Araújo, R.M.d., Rekhi, M.: Diffusion dynamics of games on online social networks. In: Proceedings of the 3rd conference on Online Social Networks, p. 2. USENIX Association (2010)
Blackburn, J., Simha, R., Kourtellis, N., Zuo, X., Ripeanu, M., Skvoretz, J., Iamnitchi, A.: Branded with a scarlet “c”: cheaters in a gaming social network. In: Proceedings of the 21st International Conference on World Wide Web (2012)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
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Zuo, X., Blackburn, J., Kourtellis, N., Skvoretz, J., Iamnitchi, A. (2014). The Influence of Indirect Ties on Social Network Dynamics. In: Aiello, L.M., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science, vol 8851. Springer, Cham. https://doi.org/10.1007/978-3-319-13734-6_4
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DOI: https://doi.org/10.1007/978-3-319-13734-6_4
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