Targeted Influential Nodes Selection in Location-Aware Social Networks

الملخص EN

Given a target area and a location-aware social network, the location-aware influence maximization problem aims to find a set of seed users such that the information spread from these users will reach the most users within the target area.

We show that the problem is NP-hard and present an approximate algorithm framework, namely, TarIM-SF, which leverages on a popular sampling method as well as spatial filtering model working on arbitrary polygons.

Besides, for the large-scale network we also present a coarsening strategy to further improve the efficiency.

We theoretically show that our approximate algorithm can provide a guarantee on the seed quality.

Experimental study over three real-world social networks verified the seed quality of our framework, and the coarsening-based algorithm can provide superior efficiency.