Extracting influential nodes on a social network for information diffusion
- 1.5k Downloads
We address the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models. The past study showed that a greedy strategy can give a good approximate solution to the problem. However, a conventional greedy method faces a computational problem. We propose a method of efficiently finding a good approximate solution to the problem under the greedy algorithm on the basis of bond percolation and graph theory, and compare the proposed method with the conventional method in terms of computational complexity in order to theoretically evaluate its effectiveness. The results show that the proposed method is expected to achieve a great reduction in computational cost. We further experimentally demonstrate that the proposed method is much more efficient than the conventional method using large-scale real-world networks including blog networks.
KeywordsSocial network analysis Information diffusion model Influence maximization problem Bond percolation
This work was partly supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 20500147), and Asian Office of Aerospace Research and Development, Air Force Office of Scientific Research, US Air Force Research Laboratory under Grant No. AOARD-08-4027.
- Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA, pp 57–66Google Scholar
- Even-Dar E, Shapira A (2007) A note on maximizing the spread of influence in social networks. Internet and Network Economics: WINE 2007, LNCS 4858, pp 281–286Google Scholar
- Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 7th international World Wide Web conference, New York, USA, pp 107–117Google Scholar
- Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, USA, pp 137–146Google Scholar
- Kempe D, Kleinberg J, Tardos E (2005) Influential nodes in a diffusion model for social networks. Automata, Languages and Programming: ICALP 2005, LNCS 3580, pp 1127–1138Google Scholar
- Leskovec J, Adamic LA, Huberman BA (2006) The dynamics of viral marketing. In: Proceedings of the 7th ACM conference on electronic commerce, Ann Arbor, Michigan, USA, pp 228–237Google Scholar
- Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose, California, USA, pp 420–429Google Scholar
- McCallum A, Corrada-Emmanuel A, Wang X (2005) Topic and role discovery in social networks. In: Proceedings of the 19th international joint conference on artificial intelligence, Edinburugh, Scotland, pp 786–791Google Scholar
- Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton, Alberta, Canada, pp 61–70Google Scholar
- Saito K, Kimura M, Motoda H (2008) Effective visualization of information diffusion process over complex networks. In: Machine learning and knowledge discovery in databases: ECML PKDD 2008, LNAI 5212, pp 326–341Google Scholar