A study on the influential neighbors to maximize information diffusion in online social networks

Computational Social Networks

Volumn 2, Issue 1, 2015, Pages

  Kim, Hyoungshick ^a   Beznosov, Konstantin ^b   Yoneki, Eiko ^c  

^a Sungkyunkwan University   (South Korea)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Information diffusion; Information dissemination; Online social networks; Viral marketing

Indexed keywords

EID: 84947241347     PISSN: None     EISSN: 21974314     Source Type: Journal    
DOI: 10.1186/s40649-015-0013-8     Document Type: Article

References (23)

1. Brown, JJ, Reingen, PH: Social ties and word-of-mouth referral behavior. J. Consum. Res. 14(3), 350–362 (1987)
2. Goldenberg, J, Libai, B, Muller, E: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Letters. 12(3), 211–223 (2001). http://link.springer.com/article/10.1023%2FA%3A1011122126881
3. Metaxas, PT, Mustafaraj, E, Gayo-Avello, D: How (not) to predict elections. In: Proceedings of the 3rd International Conference on Privacy, Security, Risk and Trust (PASSAT) and the 3rd International Conference on Social Computing (SocialCom) (2011)
4. Cao, Q, Sirivianos, M, Yang, X, Pregueiro, T: Aiding the detection of fake accounts in large scale social online services. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI) (2012)
5. Thomas, K, Grier, C, Paxson, V: Adapting social spam infrastructure for political censorship. In: Proceedings of the 5th USENIX Conference on Large-Scale Exploits and Emergent Threats (LEET) (2012)
6. Domingos, P, Richardson, M: Mining the network value of customers. In: Proceedings of the 7th ACM Conference on Knowledge Discovery and Data Mining (KDD) (2001)
7. Kempe, D, Kleinberg, J, Tardos, E: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM Conference on Knowledge Discovery and Data Mining (KDD) (2003)
8. Kim, H, Yoneki, E: Influential neighbours selection for information diffusion in online social networks. In: Proceedings of the 21th International Conference on Computer Communication Networks (ICCCN) (2012)
9. Cha, M, Haddadi, H, Benevenuto, F, Gummadi, KP: Measuring user influence in twitter: the million follower fallacy. In: Proceedings of the 4th AAAI Conference on Weblogs and Social Media (ICWSM) (2010)
10. Kim, H: Don’t count the number of friends when you are spreading information in social networks. In: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (ICUIMC) (2014)
11. Gruhl, D, Guha, R, Liben-Nowell, D, Tomkins, A: Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web (WWW) (2004)
12. Leskovec, J, Krause, A, Guestrin, C, Faloutsos, C, VanBriesen, J, Glance, N: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM Conference on Knowledge Discovery and Data Mining (KDD) (2007)
13. Chen, W, Wang, Y, Yang, S: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM Conference on Knowledge Discovery and Data Mining (KDD) (2009)
14. Kimura, M, Saito, K: Tractable models for information diffusion in social networks. In: Proceedings of the 10th European Conference on Principle and Practice of Knowledge Discovery in Databases (PKDD) (2006)
15. Chen, W, Wang, C, Wang, Y: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM Conference on Knowledge Discovery and Data Mining (KDD) (2010)
16. Wang, Y, Cong, G, Song, G, Xie, K: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of the 16th ACM Conference on Knowledge Discovery and Data Mining (KDD) (2010)
17. Kim, H, Beznosov, K, Yoneki, E: Finding influential neighbors to maximize information diffusion in twitter. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 701–706. WWW Companion ’14 (2014)
18. Romero, DM, Galuba, W, Asur, S, Huberman, BA: Influence and passivity in social media. In: Proceedings of the 20th International Conference on World Wide Web (WWW) (2011)
19. Zhou, Z, Bandari, R, Kong, J, Qian, H, Roychowdhury, V: Information resonance on Twitter: watching Iran. In: Proceedings of the First ACM Workshop on Social Media Analytics (SOMA) (2010)
20. Hansen, LK, Arvidsson, A, Nielsen, FA, Colleoni, E, Etter, M: Good friends, bad news - affect and virality in Twitter. In: Future Information Technology. Communications in Computer and Information Science, pp. 34–43 (2011). http://link.springer.com/chapter/10.1007%2F978-3-642-22309-9_5
21. Kim, H, Tang, J, Anderson, R, Mascolo, C: Centrality prediction in dynamic human contact networks. Comput. Netw. 56(3), 983–996 (2012)
22. Wehmuth, K, Ziviani, A: Daccer: Distributed assessment of the closeness centrality ranking in complex networks. Comput. Netw. 57(13), 2536–2548 (2013)
23. Boutet, A, Kim, H, Yoneki, E: What’s in Twitter, I know what parties are popular and who you are supporting now!Soc. Netw. Anal. Min. 3(4), 1379–1391 (2013)