Information Propagation Game: a Tool to Acquire Human Playing Data for Multi-Player Influence Maximization on Social Networks

摘要

With the popularity of online social network services, influence maximization on social networks has drawn much attention in recent years. Most of these studies approximate a greedy based sub-optimal solution by proving the submodular nature of the utility function. Instead of using the analytical techniques, we are interested in solving the diffusion competition and influence maximization problem by a data-driven approach. We propose Information Propagation Game (IPG), a framework that can collect a large number of seed picking strategies for analysis. Through the IPG framework, human players are not only having fun but also helping contributing the seed picking strategies. Preliminary experiment suggests that centrality based heuristics are too simple for seed selection in a multiple player environment.