De-Nian Yang
ABSTRACT
Friending recommendation has successfully contributed to the explosive growth of online social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate a recommendation support for active friending, where a user actively specifies a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target has not been explored for existing online social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, Acceptance Probability Maximization (APM), and develop a polynomial time algorithm, called Selective Invitation with Tree and In-Node Aggregation (SITINA), to find the optimal solution. We implement an active friending service with SITINA on Facebook to validate our idea. Our user study and experimental results reveal that SITINA outperforms manual selection and the baseline approach in solution quality efficiently.
- http://money.cnn.com/2012/10/03/technology/social/facebook-promoted-posts/index.html.Google Scholar
- V. Chaoji, S. Ranu, R. Rastogi, and R. Bhatt. Recommendations to boost content spread in social networks. In Proc. WWW, pages 529--538, 2012. Google ScholarDigital Library
- J. Chen, W. Geyer, C. Dugan, M. J. Muller, and I. Guy. Make new friends, but keep the old: Recommending people on social networking sites. In CHI, pages 201--210, 2009. Google ScholarDigital Library
- W. Chen, A. Collins, R. Cummings, T. Ke, Z. Liu, D. Rincón, X. Sun, Y. Wang, W. Wei, and Y. Yuan. Influence maximization in social networks when negative opinions may emerge and propagate. In SDM, pages 379--390, 2011.Google ScholarCross Ref
- W. Chen, W. Lu, and N. Zhang. Time-critical influence maximization in social networks with time-delayed diffusion process. In AAAI, 2012.Google ScholarDigital Library
- W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD, pages 1029--1038, 2010. Google ScholarDigital Library
- W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. In ICDM, pages 88--97, 2010. Google ScholarDigital Library
- J. Cheng, Y. Ke, W. Ng, and A. Lu. FG-index: Towards verification-free query processing on graph databases. In SIGMOD, pages 857--872, 2007. Google ScholarDigital Library
- R. B. Cialdini and N. J. Goldstein. Social influence: Compliance and conformity. Annual Review of Psychology, 55:591--621, 2004.Google ScholarCross Ref
- E. Cohen, E. Halperin, H. Kaplan, and U. Zwick. Reachability and distance queries via 2-hop labels. In SODA, pages 937--946, 2002. Google ScholarDigital Library
- W. Fan, J. Li, S. Ma, H. Wang, and Y. Wu. Graph homomorphism revisited for graph matching. In VLDB, pages 1161--1172, 2010. Google ScholarDigital Library
- T. L. Fond and J. Neville. Randomization tests for distinguishing social influence and homophily effects. In WWW, pages 601--610, 2010. Google ScholarDigital Library
- A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. Learning influence probabilities in social networks. In WSDM, pages 241--250, 2010. Google ScholarDigital Library
- A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. A data-based approach to social influence maximization. In VLDB, pages 73--84, 2011. Google ScholarDigital Library
- I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman. Personalized recommendation of social software items based on social relations. In RecSys, pages 53--60, 2009. Google ScholarDigital Library
- S. Hangal, D. MacLean, M. S. Lam, and J. Heer. All friends are not equal: Using weights in social graphs to improve search. In Proc. SNA-KDD Workshop, 2010.Google Scholar
- D. Kempe, J. M. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In KDD, pages 137--146, 2003. Google ScholarDigital Library
- J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. M. VanBriesen, and N. S. Glance. Cost-effective outbreak detection in networks. In KDD, pages 420--429, 2007. Google ScholarDigital Library
- K. Macropol and A. K. Singh. Scalable discovery of best clusters on large graphs. In VLDB, pages 693--702, 2010. Google ScholarDigital Library
- P. V. Marsden and N. E. Friedkin. Network studies of social influence. Sociological Methods & Research, 22(1):127--151, 1993.Google ScholarCross Ref
- M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, pages 415--444, 2001.Google ScholarCross Ref
- A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In IMC, pages 29--42, 2007. Google ScholarDigital Library
- R. Ronen and O. Shmueli. Evaluating very large datalog queries on social networks. In EDBT, pages 577--587, 2009. Google ScholarDigital Library
- K. Saito, R. Nakano, and M. Kimura. Prediction of information diffusion probabilities for independent cascade model. In KES, pages 67--75, 2008. Google ScholarDigital Library
- E. Spertus, M. Sahami, and O. Buyukkokten. Evaluating similarity measures: A large-scale study in the orkut social network. In KDD, pages 678--684, 2005. Google ScholarDigital Library
- J. Tang, H. Gao, X. Hu, and H. Liu. Exploiting homophily effect for trust prediction. In WSDM, pages 53--62, 2013. Google ScholarDigital Library
- B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in facebook. In WOSN, pages 37--42, 2009. Google ScholarDigital Library
- Z. Wen and C.-Y. Lin. On the quality of inferring interests from social neighbors. In KDD, pages 373--382, 2010. Google ScholarDigital Library
- B. Xu, A. Chin, H. Wang, and L. Zhang. Social linking and physical proximity in a mobile location-based service. In Proc. Ubicomp, pages 1--10, 2011. Google ScholarDigital Library
- D.-N. Yang, H.-J. Hung, W.-C. Lee, and W. Chen. Maximizing acceptance probability for active friending in on-line social networks. CoRR, abs/1302.7025, 2013.Google Scholar
- S.-H. Yang, A. J. Smola, B. Long, H. Zha, and Y. Chang. Friend or frenemy?: Predicting signed ties in social networks. In SIGIR, pages 555--564, 2012. Google ScholarDigital Library
- G. K. Zipf. Human behavior and the principle of least effort. Addison-Wesley Press, 1949.Google Scholar
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- Maximizing acceptance probability for active friending in online social networks
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