Trial and error in influential social networks

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.