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
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 on a social network for information diffusion
Data Mining and Knowledge Discovery
On the Approximability of Influence in Social Networks
SIAM Journal on Discrete Mathematics
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
Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A cutting-plane algorithm for solving a weighted influence interdiction problem
Computational Optimization and Applications
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With their blistering expansions in recent years, popular on-line social sites such as Twitter, Facebook and Bebo, have become some of the major news sources as well as the most effective channels for viral marketing nowadays. However, alongside these promising features comes the threat of misinformation propagation which can lead to undesirable effects, such as the widespread panic in the general public due to faulty swine flu tweets on Twitter in 2009. Due to the huge magnitude of online social network (OSN) users and the highly clustered structures commonly observed in these kinds of networks, it poses 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 β1T -- Node Protectors, which aims to find the smallest set of highly influential nodes whose decontamination with good information helps to contain the viral spread of misinformation, initiated from the set I, to a desired ratio (1 − β) in T time steps. In this family set, we analyze and present solutions including inapproximability result, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based heuristic method for the Node Protector problems. To verify our suggested solutions, we conduct experiments on real world traces including NetHEPT, NetHEPT_WC and Facebook networks. Empirical results indicate that our methods are among the best ones for hinting out those important nodes in comparison with other available methods.