Increasing Coverage of Information Spreading in Social Networks with Supporting Seeding
Campaigns based on information spreading processes within online networks have become a key feature of marketing landscapes. Most research in the field has concentrated on propagation models and improving seeding strategies as a way to increase coverage. Proponents of such research usually assume selection of seed set and the initialization of the process without any additional support in following stages. The approach presented in this paper shows how initiation by seed set process can be supported by selection and activation of additional nodes within network. The relationship between the number of additional activations and the size of initial seed set is dependent on network structures and propagation parameters with the highest performance observed for networks with low average degree and smallest propagation probability in a chosen model.
KeywordsInformation spreading Supporting seeding Viral marketing Word of mouth Complex networks
This work was partially supported by the National Science Centre, Poland, grant no. 2016/21/B/HS4/01562.
- 3.Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM (2010)Google Scholar
- 14.Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
- 16.Leskovec J., Kleinberg J., Faloutsos C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187. ACM (2005)Google Scholar
- 17.Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In Advances in Neural Information Processing Systems, pp. 539–547 (2012)Google Scholar
- 19.Ley, M.: The DBLP computer science bibliography: evolution, research issues, perspectives. In: International Symposium on String Processing and Information Retrieval, pp. 1–10 (2002)Google Scholar
- 23.Pfitzner, R., Garas, A., Schweitzer, F.: Emotional divergence influences information spreading in twitter. In: Proceedings of Sixth International Conference on Weblogs and Social Media, pp. 2–5 (2012)Google Scholar
- 24.Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)Google Scholar
- 26.Seeman, L., Singer, Y.: Adaptive seeding in social networks. In Foundations of Computer Science (FOCS), IEEE 54th Annual Symposium, pp. 459–468. IEEE (2013)Google Scholar
- 27.Subelj, L. Bajec, M.: Software systems through complex networks science: Review, analysis and applications. In: Proceedings of the First International Workshop on Software Mining, pp. 9–16. ACM (2012)Google Scholar
- 28.Tang, J., Musolesi, M., Mascolo, C., Latora, V., Nicosia, V.: Analysing information flows and key mediators through temporal centrality metrics. In: Proceedings of the 3rd Workshop on Social Network Systems, p. 3. ACM (2010)Google Scholar
- 29.Watts, D.J., Peretti, J., Frumin, M.: Viral Marketing for the Real World. Harvard Business School Pub, Boston (2007)Google Scholar
- 30.Zhang, J.-X., Duan-Bing Chen, Q.D., Zhao, Z.-D.: Identifying a set of influential spreaders in complex networks. Sci. Rep. 6 (2016)Google Scholar