STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Mining the network value of customers
Proceedings of the seventh 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
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
On the approximability of influence in social networks
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Word of Mouth: Rumor Dissemination in Social Networks
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
Encounter-based worms: Analysis and defense
Ad Hoc Networks
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Community detection algorithms: a comparative analysis: invited presentation, extended abstract
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
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
@spam: the underground on 140 characters or less
Proceedings of the 17th ACM conference on Computer and communications security
Toward worm detection in online social networks
Proceedings of the 26th Annual Computer Security Applications Conference
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Malware propagation in online social networks: nature, dynamics, and defense implications
Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security
Overlapping communities in dynamic networks: their detection and mobile applications
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Competing for customers in a social network: the quasi-linear case
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
Models and analysis of active worm defense
MMM-ACNS'05 Proceedings of the Third international conference on Mathematical Methods, Models, and Architectures for Computer Network Security
Cheap, easy, and massively effective viral marketing in social networks: truth or fiction?
Proceedings of the 23rd ACM conference on Hypertext and social media
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With their blistering expansion in recent years, popular online social sites such as Twitter, Facebook and Bebo, have become not only one of the most effective channels for viral marketing but also the major news sources for many people nowadays. Alongside these promising features, however, comes the threat of misinformation propagation which can lead to undesirable effects. Due to the sheer number of online social network (OSN) users and the highly clustered structures commonly shared by these kinds of networks, there is a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks. In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the @b"T^I-Node Protectors problems, which aim to find the smallest set of highly influential nodes from which disseminating good information helps to contain the viral spread of misinformation, initiated from a set of nodes I, within a desired fraction (1-@b) of the nodes in the entire network in T time steps. For this family of problems, we analyze and present solutions including their inapproximability results, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based method for solving these problems. We further conduct a number of experiments on real-world traces, and the empirical results show that our proposed methods outperform existing alternative approaches in finding those important nodes that can help to contain the spread of misinformation effectively.