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
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
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
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
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
Social Recommendation with Interpersonal Influence
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A study of rumor control strategies on social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
CELF++: optimizing the greedy algorithm for influence maximization in social networks
Proceedings of the 20th international conference companion on World wide web
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Exploring social influence via posterior effect of word-of-mouth recommendations
Proceedings of the fifth ACM international conference on Web search and data mining
Exploring social influence for recommendation: a generative model approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Mining topic-level opinion influence in microblog
Proceedings of the 21st ACM international conference on Information and knowledge management
Popularity prediction in microblogging network: a case study on sina weibo
Proceedings of the 22nd international conference on World Wide Web companion
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Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracy In this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.