Linear Threshold Model In Temporal Networks - Seed Selection For Social Influence

The problem of finding optimal set of users for influencing others in social networks has been studied for more than ten years. As it has been shown, it is a NP-hard problem, so since than some heuristics were proposed as suboptimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the temporal one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time. An alternative and more realistic approach, recently extensively explored, are temporal networks, i.e. networks that reflect the occurrence of events in time [1] and change in its nodes and edges.