Finding maximum-cardinality matchings in undirected graphs is arguably one of the most central graph primitives. For m-edge and n-vertex graphs, it is well-known to be solvable in O(m root n) time;however, for several...
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Finding maximum-cardinality matchings in undirected graphs is arguably one of the most central graph primitives. For m-edge and n-vertex graphs, it is well-known to be solvable in O(m root n) time;however, for several applications this running time is still too slow. We investigate how linear-time (and almost linear-time) data reduction (used as preprocessing) can alleviate the situation. More specifically, we focus on linear-time kernelization. We start a deeper and systematic study both for general graphs and for bipartite graphs. Our data reduction algorithms easily comply (in form of preprocessing) with every solution strategy (exact, approximate, heuristic), thus making them attractive in various settings.
Given a graph G = (V,E), four distinct vertices u(1),u(2),u(3),u(4) is an element of V and four natural numbers n(1),n(2),n(3),n(4) such that Sigma(i=1)(4)n(i) = \V\, we wish to find a partition V-1,V-2,V-3,V-4 of the...
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Given a graph G = (V,E), four distinct vertices u(1),u(2),u(3),u(4) is an element of V and four natural numbers n(1),n(2),n(3),n(4) such that Sigma(i=1)(4)n(i) = \V\, we wish to find a partition V-1,V-2,V-3,V-4 of the vertex set V such that u(i) is an element of V-i, \V-i\ = n(i) and V-i induces a connected subgraph of G for each i, 1 less than or equal to i less than or equal to 4. In this paper we give a simple linear-time algorithm to find such a partition if G is a 4-connected planar graph and u(1), u(2), u(3) and u(4) are located on the same face of a plane embedding of G. Our algorithm is based on a ''4-canonical decomposition'' of G, which is a generalization of an st-numbering and a ''canonical 4-ordering'' known in the area of graph drawings. (C) 1997 Elsevier Science B.V.
A bipartite graph G=(U,W,E) with vertex set V=Ua(a)W is convex if there exists an ordering of the vertices of W such that for each uaU, the neighbors of u are consecutive in W. A compact representation of a convex bip...
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A bipartite graph G=(U,W,E) with vertex set V=Ua(a)W is convex if there exists an ordering of the vertices of W such that for each uaU, the neighbors of u are consecutive in W. A compact representation of a convex bipartite graph for specifying such an ordering can be computed in O(|V|+|E|) time. The paired-domination problem on bipartite graphs has been shown to be NP-complete. The complexity of the paired-domination problem on convex bipartite graphs has remained unknown. In this paper, we present an O(|V|) time algorithm to solve the paired-domination problem on convex bipartite graphs given a compact representation. As a byproduct, we show that our algorithm can be directly applied to solve the total domination problem on convex bipartite graphs in the same time bound.
The Abstract Voronoi diagrams are a general framework covering many types of concrete diagrams for different types of sites or distance measures. Generalizing a famous result by Aggarwal et al. [1] we prove the follow...
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The Abstract Voronoi diagrams are a general framework covering many types of concrete diagrams for different types of sites or distance measures. Generalizing a famous result by Aggarwal et al. [1] we prove the following. Suppose it is known that inside a closed domain D the Voronoi diagram V (S) is a tree, and for each subset S' subset of S, a forest with one face per site. If the order of Voronoi regions of V(S) along the boundary of D is given, then V (S) inside D can be constructed in lineartime. (c) 2017 Elsevier B.V. All rights reserved.
A planar orthogonal drawing of a planar 4-graph G (i.e., a planar graph with vertex-degree at most four) is a crossing-free drawing that maps each vertex of G to a distinct point of the plane and each edge of G to a p...
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A planar orthogonal drawing of a planar 4-graph G (i.e., a planar graph with vertex-degree at most four) is a crossing-free drawing that maps each vertex of G to a distinct point of the plane and each edge of G to a polygonal chain consisting of horizontal and vertical segments. A longstanding open question in Graph Drawing, dating back over 30 years, is whether there exists a linear-time algorithm to compute an orthogonal drawing of a plane 4-graph with the minimum number of bends. The term "plane" indicates that the input graph comes together with a planar embedding, which must be preserved by the drawing (i.e., the drawing must have the same set of faces as the input graph). In this paper we positively answer the question above for the widely-studied class of series-parallel graphs. Our linear-time algorithm is based on a characterization of the planar series-parallel graphs that admit an orthogonal drawing without bends. This characterization is given in terms of the orthogonal spirality that each type of triconnected component of the graph can take;the orthogonal spirality of a component measures how much that component is "rolled-up" in an orthogonal drawing of the graph.
The subject of the paper is to propose an O(Absolute value of V + Absolute value of E) algorithm for the 3-edge-connectivity augmentation problem (UW-3-ECA) defined by ''Given an undirected graph G0 = (V, E), ...
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The subject of the paper is to propose an O(Absolute value of V + Absolute value of E) algorithm for the 3-edge-connectivity augmentation problem (UW-3-ECA) defined by ''Given an undirected graph G0 = (V, E), find an edge set E' of minimum cardinality such that the graph (V,E or E') (denoted as G0 + E') is 3-edge-connected, where each edge of E' connects distinct vertices of V.'' Such a set E' is called a solution to the problem. Let UW-3-ECA(S) (UW-3-ECA(M), respectively) denote UW-3-ECA in which G0 + E' is required to be simple (G0 + E' may have multiple edges). Note that we can assume that G0 is simple in UW-3-ECA(S). UW-3-ECA(M) is divided into two subproblems (1) and (2) as follows: (1) finding all k-edge-connected components of a given graph for every k less-than-or-equal-to 3, and (2) determining a minimum set of edges whose addition to 66 result in a 3-edge-connected graph. Concerning the subproblem (1), we use an O(Absolute value of V + Absolute value of E) algorithm that has already been existing. The paper proposes an 0 (Absolute value of V + Absolute value of E) algorithm for the subproblem (2). Combining these algorithms makes an O(Absolute value of V + Absolute value of E) algorithm for finding a solution to UW-3-ECA (M). Furthermore, it is shown that a solution E' to UW-3-ECA (M) is also a solution to UW-3-ECA(S) if Absolute value of V greater-than-or-equal-to 4, partly solving an open problem UW-k-ECA(S) that is a generalization of UW-3-ECA (S).
The broadcast domination problem is a variant of the classical minimum dominating set problem in which a transmitter of power p at vertex v is capable of dominating (broadcasting to) all vertices within distance p fro...
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The broadcast domination problem is a variant of the classical minimum dominating set problem in which a transmitter of power p at vertex v is capable of dominating (broadcasting to) all vertices within distance p from v. Our goal is to assign a broadcast power f(v) to every vertex v in a graph such that Sigma(v is an element of v) f(v) is minimized, and such that every vertex u with f(u) = 0 Is within distance f(v) of some vertex v with f(v) > 0. The problem is solvable in polynomial time on a general graph (Heggernes and Lokshtanov, Disc Math (2006), 3267-3280) and Blair et al. (Congr. Num. (2004), 55-77.) gave an O(n(2)) algorithm for trees. In this article, we provide an O(n) algorithm for trees. Our algorithm is notable due to the fact that it makes decisions for each vertex v based on "nonlocal" information from vertices far away from v, whereas almost all other linear-time algorithms for trees only make use of local information. (C) 2008 Wiley Periodicals, Inc. NETWORKS, Vol. 53(2), 160-169 2009
The computational problem of finding the center of a graph is motivated by a number of facility-location problems. We exploit a new characterization of interval graphs for the purpose of obtaining a linear-time algori...
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The computational problem of finding the center of a graph is motivated by a number of facility-location problems. We exploit a new characterization of interval graphs for the purpose of obtaining a linear-time algorithm for computing both the center and the diameter of an interval graph.
Weakly quasi-threshold graphs form a proper subclass of the well-known class of cographs by restricting the join operation. In this paper we characterize weakly quasi-threshold graphs by a finite set of forbidden subg...
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Weakly quasi-threshold graphs form a proper subclass of the well-known class of cographs by restricting the join operation. In this paper we characterize weakly quasi-threshold graphs by a finite set of forbidden subgraphs: the class of weakly quasi-threshold graphs coincides with the class of {P-4, co-(2P(3))}-free graphs. Moreover we give the first linear-time algorithm to decide whether a given graph belongs to the class of weakly quasi-threshold graphs, improving the previously known running time. Based on the simplicity of our recognition algorithm, we can provide certificates of membership (a structure that characterizes weakly quasi-threshold graphs) or non-membership (forbidden induced subgraphs) in additional O(n) time. Furthermore we give a linear-time algorithm for finding the largest induced weakly quasi-threshold subgraph in a cograph.
A graph G is P4-sparse if no set of five vertices in G induces more than one chordless path of length three. P4-sparse graphs generalize both the class of cographs and the class of P4-reducible graphs. One remarkable ...
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A graph G is P4-sparse if no set of five vertices in G induces more than one chordless path of length three. P4-sparse graphs generalize both the class of cographs and the class of P4-reducible graphs. One remarkable feature of P4-sparse graphs is that they admit a tree representation unique up to isomorphism. It has been shown that this tree representation can be obtained in polynomial time. This paper gives a lineartime algorithm to recognize P4-sparse graphs and shows how the data structures returned by the recognition algorithm can be used to construct the corresponding tree representation in lineartime.
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