We introduce a cops and robbers game with one cop and one robber on a special type of time-varying graphs (TVGs), namely edge-periodic graphs. These are TVGs in which, for each edge e, a binary string τ(e) is given such that the edge e is present in time step t if and only if τ(e) contains a 1 at position tmod|τ(e)|. This periodicity allows for a compact representation of infinite TVGs. We prove that even for very simple underlying graphs, i.e., directed and undirected cycles, the problem of deciding whether a cop-winning strategy exists is NP-hard and W[1]-hard parameterized by the number of vertices. Furthermore, we show that this decision problem can be solved on general edge-periodic graphs in PSPACE. Finally, we present tight bounds on the minimum length of a directed or undirected cycle that guarantees the cycle to be robber-winning.

We study the fixed-parameter tractability of the problem of deciding whether a given temporal graph admits a temporal walk that visits all vertices (temporal exploration) or, in some variants, a certain subset of the vertices. In the strict variant, edges must be traversed in strictly increasing timesteps; in the non-strict variant, any number of edges can be traversed in each timestep. For both variants, we give FPT algorithms for finding a temporal walk that visits a given set X of vertices, parameterized by |X|, and for finding a temporal walk that visits at least k distinct vertices, parameterized by k. We also show W[2]-hardness for a set version of temporal exploration. For the non-strict variant, we give an FPT algorithm for temporal exploration parameterized by the lifetime, and show that temporal exploration can be solved in polynomial time if the graph in each timestep has at most two connected components.

Abstract A temporal graph with lifetime L is a sequence of L graphs $$G_1, \ldots ,G_L$$ G 1 , … , G L , called layers, all of which have the same vertex set V but can have different edge sets. The underlying graph is the graph with vertex set V that contains all the edges that appear in at least one layer. The temporal graph is always connected if each layer is a connected graph, and it is k -edge-deficient if each layer contains all except at most k edges of the underlying graph. For a given start vertex s , a temporal exploration is a temporal walk that starts at s , traverses at most one edge in each layer, and visits all vertices of the temporal graph. We show that always-connected, k -edge-deficient temporal graphs with sufficient lifetime can always be explored in $$O(kn \log n)$$ O ( k n log n ) time steps. We also construct always-connected, k -edge-deficient temporal graphs for which any exploration requires $$\varOmega (n \log k)$$ Ω ( n log k ) time steps. For always-connected, 1-edge-deficient temporal graphs, we show that O ( n ) time steps suffice for temporal exploration.

A temporal graph is a graph whose edge set can change over time. We only require that the edge set in each time step forms a connected graph. The temporal exploration problem asks for a temporal walk that starts at a given vertex, moves over at most one edge in each time step, visits all vertices, and reaches the last unvisited vertex as early as possible. We show in this paper that every temporal graph with n vertices can be explored in O(n^{1.75}) time steps provided that either the degree of the graph is bounded in each step or the temporal walk is allowed to make two moves per step. This result is interesting because it breaks the lower bound of Omega(n^2) steps that holds for the worst-case exploration time if only one move per time step is allowed and the graph in each step can have arbitrary degree. We complement this main result by a logarithmic inapproximability result and a proof that for sparse temporal graphs (i.e., temporal graphs with O(n) edges in the underlying graph) making O(1) moves per time step can improve the worst-case exploration time at most by a constant factor.

A temporal graph can be viewed as a sequence of static graphs indexed by discrete time steps. The vertex set of each graph in the sequence remains the same; however, the edge sets are allowed to differ. A natural problem on temporal graphs is the Temporal Exploration problem (TEXP): given, as input, a temporal graph G of order n, we are tasked with computing an exploration schedule (i.e., a temporal walk that visits all vertices in G), such that the time step at which the walk arrives at the last unvisited vertex is minimised (we refer to this time step as the arrival time). It can be easily shown that general temporal graphs admit exploration schedules with arrival time no greater than O(n^2). Moreover, it has been shown previously that there exists an infinite family of temporal graphs for which any exploration schedule has arrival time Omega(n^2), making these bounds tight for general TEXP instances. We consider restricted instances of TEXP, in which the temporal graph given as input is, in every time step, of maximum degree d; we show an O(n^2/log n) bound on the arrival time when d is constant, and an O(d log d * n^2/log n) bound when d is given as some function of n.