Positive Influence Dominating Set in Online Social Networks
Online social network has developed significantly in recent years as a medium of communicating, sharing and disseminating information and spreading influence. Most of current research has been on understanding the property of online social network and utilizing it to spread information and ideas. In this paper, we explored the problem of how to utilize online social networks to help alleviate social problems in the physical world, for example, the drinking, smoking, and drug related problems. We proposed a Positive Influence Dominating Set (PIDS) selection algorithm and analyzed its effect on a real online social network data set through simulations. By comparing the size and the average positive degree of PIDS with those of a 1-dominating set, we found that by strategically choosing 26% more people into the PIDS to participate in the intervention program, the average positive degree increases by approximately 3.3 times. In terms of the application, this result implies that by moderately increasing the participation related cost, the probability of positive influencing the whole community through the intervention program is significantly higher. We also discovered that a power law graph has empirically larger dominating sets (both the PIDS and 1-dominating set) than a random graph does.
KeywordsGreedy Algorithm Random Graph Binge Drinker Node Degree Online Social Network
Unable to display preview. Download preview PDF.
- 1.Almada, L., Camacho, E., Rodriguez, R., Thompson, M., Voss, L.: Deterministic and Small-World Network Models of College Drinking Patterns. In: AMSSI Technical Report (2007), http://www.amssi.org/reports/alcohol2006.pdf
- 2.Anagnostopoulos, A., Kumar, R., Mahdian, M.: Influence and Correlation in Social Networks. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7–15 (2008)Google Scholar
- 5.Eubank, S., Anil Kumar, V.S., Marathe, M.V., Srinivasan, A., Wang, N.: Structural and Algorithmic Aspects of Massive Social Networks. In: Proceedings of the 15th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 718–727 (2004)Google Scholar
- 8.Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the Spread of Influence through a Social Network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 137–146 (2003)Google Scholar
- 10.Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and Analysis of Online Social Networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet Measurement Conference, pp. 29–42 (2007)Google Scholar
- 11.Nazir, A., Raza, S., Chuah, C.N.: Unveiling Facebook: A Measurement Study of Social Network Based Applications. In: Proceedings of the 8th ACM SIGCOMM Internet Measurement Conference, pp. 43–56 (2008)Google Scholar