Temporal Vertex Cover with a Sliding Time Window

TL;DR

This paper introduces and analyzes two temporal vertex cover problems on dynamic graphs, focusing on minimizing vertex appearances to cover edges over time, with applications in sensor and transportation networks.

Contribution

It defines two new temporal vertex cover variants, analyzes their computational complexity, and provides approximation and exact algorithms, including some with near-optimal performance under ETH.

Findings

Both problems are computationally hard with strong hardness results.

Polynomial-time algorithms are developed for certain cases.

Some algorithms are nearly optimal assuming ETH.

Abstract

Modern, inherently dynamic systems are usually characterized by a network structure, i.e. an underlying graph topology, which is subject to discrete changes over time. Given a static underlying graph $G$, a temporal graph can be represented via an assignment of a set of integer time-labels to every edge of $G$, indicating the discrete time steps when this edge is active. While most of the recent theoretical research on temporal graphs has focused on the notion of a temporal path and other "path-related" temporal notions, only few attempts have been made to investigate "non-path" temporal graph problems. In this paper, motivated by applications in sensor and in transportation networks, we introduce and study two natural temporal extensions of the classical problem Vertex Cover. In both cases we wish to minimize the total number of "vertex appearances" that are needed to "cover" the whole temporal graph. In our first problem, Temporal Vertex Cover, the aim is to cover every edge at least once during the lifetime of the temporal graph, where an edge can be covered by one of its endpoints, only at a time step when it is active. In our second, more pragmatic variation Sliding Window Temporal Vertex Cover, we are also given a natural number $\Delta$, and our aim is to cover every edge at least once at every $\Delta$ consecutive time steps. We present a thorough investigation of the computational complexity and approximability of these two temporal covering problems. In particular, we provide strong hardness results, complemented by various approximation and exact algorithms. Some of our algorithms are polynomial-time, while others are asymptotically almost optimal under the Exponential Time Hypothesis (ETH) and other plausible complexity assumptions.