Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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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.