Heuristics for Covering the Timeline in Temporal Graphs

We consider a variant of the Vertex Cover problem on temporal graphs, called Minimum Timeline Cover (k-MinTimelineCover). Temporal graphs are used to model complex systems, describing how edges (relations) change in a discrete time domain. The k-MinTimelineCover problem has been introduced in complex data summarization and synthesis jobs. Given a temporal graph G, k-MinTimelineCover asks to define k activity intervals for each vertex, such that each temporal edge is covered by at least one active interval. The objective function is the minimization of the sum of interval lengths. k-MinTimelineCover is NP-hard and even hard to approximate within any factor for k > 1. While the literature has mainly focused on the cases k = 1, in this contribution we consider the case k > 1. We first present an ILP formulation that is able to solve the problem on moderate size instances. Then we develop an efficient heuristic, based on local search which is built on top of the solution of an existing literature method. Finally, we present an experimental evaluation of our algorithms on synthetic data sets, that shows in particular that our heuristic has a consistent improvement on the state-of-the art method.