The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
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
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
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
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
The Journal of Machine Learning Research
A note on maximizing the spread of influence in social networks
Information Processing Letters
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
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Which targets to contact first to maximize influence over social network
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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Influence maximization is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under a certain diffusion model. The Greedy algorithm for influence maximization first proposed by Kempe, later improved by Leskovec suffers from two sources of computational deficiency: 1) the need to evaluate many candidate nodes before selecting a new seed in each round, and 2) the calculation of the influence spread of any seed set relies on Monte-Carlo simulations. In this work, we tackle both problems by devising efficient algorithms to compute influence spread and determine the best candidate for seed selection. The fundamental insight behind the proposed algorithms is the linkage between influence spread determination and belief propagation on a directed acyclic graph (DAG). Experiments using real-world social network graphs with scales ranging from thousands to millions of edges demonstrate the superior performance of the proposed algorithms with moderate computation costs.