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
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
The role of compatibility in the diffusion of technologies through social networks
Proceedings of the 8th ACM conference on Electronic commerce
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithmic Game Theory
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An exact almost optimal algorithm for target set selection in social networks
Proceedings of the 10th ACM conference on Electronic commerce
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Inferring networks of diffusion and influence
Proceedings of the 16th 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
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Which Networks are Least Susceptible to Cascading Failures?
FOCS '11 Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
Information transfer in social media
Proceedings of the 21st international conference on World Wide Web
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Identifying the k most influential individuals in a social network is a well-studied problem. The objective is to detect k individuals in a (social) network who will influence the maximum number of people, if they are independently convinced of adopting a new strategy (product, idea, etc). There are cases in real life, however, where we aim to instigate groups instead of individuals to trigger network diffusion. Such cases abound, e.g., billboards, TV commercials and newspaper ads are utilized extensively to boost the popularity and raise awareness. In this paper, we generalize the "influential nodes" problem. Namely we are interested to locate the most "influential groups" in a network. As the first paper to address this problem: we (1) propose a fine-grained model of information diffusion for the group-based problem, (2) show that the process is submodular and present an algorithm to determine the influential groups under this model (with a precise approximation bound), (3) propose a coarse-grained model that inspects the network at group level (not individuals) significantly speeding up calculations for large networks, (4) show that the diffusion function we design here is submodular in general case, and propose an approximation algorithm for this coarse-grained model, and finally by conducting experiments on real datasets, (5) demonstrate that seeding members of selected groups to be the first adopters can broaden diffusion (when compared to the influential individuals case). Moreover, we can identify these influential groups much faster (up to 12 million times speedup), delivering a practical solution to this problem.