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
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithmic Game Theory
On the approximability of influence in social networks
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social and Economic Networks
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
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
On the complexity of trial and error
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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In this paper, we introduce a trial-and-error model to study information diffusion in a social network. Specifically, in every discrete period, all individuals in the network concurrently try a new technology or product with certain respective probabilities. If it turns out that an individual observes a better utility, he will then adopt the trial; otherwise, the individual continues to choose his prior selection. We first demonstrate that the trial and error behavior of individuals characterizes certain global community structures of a social network, from which we are able to detect macro-communities through the observation of micro-behavior of individuals. We run simulations on classic benchmark testing graphs, and quite surprisingly, the results show that the trial and error dynamics even outperforms the Louvain method (a popular modularity maximization approach) if individuals have dense connections within communities. This gives a solid justification of the model. We then study the influence maximization problem in the trial-and-error dynamics. We give a heuristic algorithm based on community detection and provide experiments on both testing and large scale collaboration networks. Simulation results show that our algorithm significantly outperforms several well-studied heuristics including degree centrality and distance centrality in almost all of the scenarios. Our results reveal the relation between the budget that an advertiser invests and marketing strategies, and indicate that the mixing parameter, a benchmark evaluating network community structures, plays a critical role for information diffusion.