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
Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
The predictive value of young and old links in a social network
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
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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.