Maximizing the spread of influence through a social network
Proceedings of the ninth 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
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
Finding effectors in social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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Influence propagation and maximization is a well-studied problem in social network mining. However, most of the previous works focus only on homogeneous social networks where nodes and links are of single type. This work aims at defining information propagation for heterogeneous social networks (containing multiple types of nodes and links). We propose to consider the individual behaviors of persons to model the influence propagation. Person nodes possess different influence probabilities to activate their friends according to their interaction behaviors. The proposed model consists of two stages. First, based on the heterogeneous social network, we create a human-based influence graph where nodes are of human-type and links carry weights that represent how special the target node is to the source node. Second, we propose two entropy-based heuristics to identify the disseminators in the influence graph to maximize the influence spread. Experimental results show promising results for the proposed method.