Abstract
Study on information propagation in social networks has a long history. The influence maximization problem has become a popular research area for many scholars. Most of algorithms to solve the problem are based on the basic greedy algorithm raised by David Kempe etc. However, these algorithms seem to be ineffective for the large-scaled networks. On seeing the bottleneck of these algorithms, some scholars raised some heuristic algorithms. However, these heuristic algorithms just consider local information of networks and cannot get good results. In this paper, we studied the procedure of information propagation in layered cascade model, a new propagation model in which we can consider the global information of networks. Based on the analysis on layered cascade model, we developed heuristic algorithms to solve influence maximization problem, which perform well in experiments.
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Yang, H., Wang, C., Xie, J. (2012). Maximizing Influence Spread in a New Propagation Model. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_37
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DOI: https://doi.org/10.1007/978-3-642-31900-6_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31899-3
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