Titlebook: ;

Irrigate

Milling a Graph with Turn Costs: A Parameterized Complexity Perspectiveits vertices with a minimum number of ., as specified in the graph model by a 0/1 turncost function .. at each vertex . giving, for each ordered pair of edges (.,.) incident at ., the . at . of a walk that enters the vertex on edge . and departs on edge .. We describe an initial study of the parameterized complexity of the problem.

oxidize

lipoatrophy

樣式

https://doi.org/10.1007/978-94-009-8198-0ected cubic graphs. We also present dynamic programming algorithms to count the number of edge .-colorings and total .-colorings for graphs of bounded pathwidth. These algorithms can be used to obtain fast exact exponential time algorithms for counting edge .-colorings and total .-colorings on graphs, if . is small.

enterprise

https://doi.org/10.1007/978-3-662-68035-3mutation graphs. Our algorithm runs in linear time. We stress that the cutwidth problem is NP-complete on bipartite graphs and its computational complexity is open even on small subclasses of permutation graphs, such as trivially perfect graphs.

GEON

patriot

GROVE

Computing the Cutwidth of Bipartite Permutation Graphs in Linear Timemutation graphs. Our algorithm runs in linear time. We stress that the cutwidth problem is NP-complete on bipartite graphs and its computational complexity is open even on small subclasses of permutation graphs, such as trivially perfect graphs.

平靜生活

Generalized Graph Clustering: Recognizing (,,,)-Cluster Graphsr of false positives and negatives in total, while bounding the number of these locally for each cluster by . and .. We show that recognizing (.,.)-cluster graphs is NP-complete when . and . are input. On the positive side, we show that (0,.)-cluster, (.,1)-cluster, (.,2)-cluster, and (1,3)-cluster graphs can be recognized in polynomial time.

桶去微染