Information cascade at group scale

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.