Titlebook: ;

Awning

The Complexity of Gerrymandering over Graphs: Paths and Trees,any districts as possible. While typically studied from a geographical point of view, addressing social network structures, the investigation of gerrymandering over graphs was recently initiated by Cohen-Zemach et al.?[AAMAS?2018]. Settling three open questions of Ito et al.?[AAMAS?2019, TCS?2021],

纖細

Feedback Vertex Set on Hamiltonian Graphs,planar and regular. Moreover, we study the less known class of?.-Hamiltonian-ordered graphs, which are graphs that admit for any .-tuple of vertices a Hamiltonian cycle visiting them in the order given by the tuple. We prove that . remains .-hard in these restricted cases, even if a Hamiltonian cycl

infringe

Towards Classifying the Polynomial-Time Solvability of Temporal Betweenness Centrality,rtices. Betweenness centrality is efficiently computable and it is a fundamental tool in network science. Continuing and extending previous work, we study the efficient computability of betweenness centrality in . graphs (graphs with fixed vertex set but time-varying arc sets). Unlike in the static

善辯

休息

Linearizable Special Cases of the Quadratic Shortest Path Problem, sum of the interaction costs over all pairs of arcs on the path is minimized. We consider special cases of the QSPP that are linearizable as a shortest path problem in the sense of Bookhold. If the QSPP on a directed graph is linearizable under all possible choices of the arc interaction costs, the

庇護

Coma704

宮殿般

Biophysics for the Life Sciencesains all graphs whose blocks belong to . and the class . contains all graphs where the removal of a vertex creates a graph in .. Given a hereditary graph class ., we recursively define . so that . and, if ., .. The . of a graph . to a graph class . is the minimum . such that . and can be seen as an

寡頭政治

https://doi.org/10.1007/978-1-4612-5166-8d coverage requirements ., the goal is to find a minimum-sized set of vertices that are incident on at least . edges of color ., for each ., i.e., we need to cover at least . edges of color .. . is similar to . except here we are given a vertex-colored graph and the goal is to cover at least . verti

Exposition