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11
Maintaining Center and Median in Dynamic Trees
, 2000
"... We show how to maintain centers and medians for a collection of dynamic trees where edges may be inserted and deleted and node and edge weights may be changed. All updates are supported in O(log n) time, where n is the size of the tree(s) involved in the update. ..."
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Cited by 16 (3 self)
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We show how to maintain centers and medians for a collection of dynamic trees where edges may be inserted and deleted and node and edge weights may be changed. All updates are supported in O(log n) time, where n is the size of the tree(s) involved in the update.
Maintaining information in fullydynamic trees with top trees
, 2003
"... We introduce top trees as a new data structure that makes it simpler to maintain many kinds of information in a fullydynamic forest. As prime examples, we show how to maintain the diameter, center, and median of each tree in the forest. The forest can be updated by insertion and deletion of edges a ..."
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Cited by 12 (0 self)
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We introduce top trees as a new data structure that makes it simpler to maintain many kinds of information in a fullydynamic forest. As prime examples, we show how to maintain the diameter, center, and median of each tree in the forest. The forest can be updated by insertion and deletion of edges and by changes to vertex and edge weights. Each update is supported in O(log n) time, where n is the size of the tree(s) involved in the update. Also, we show how to support nearest common ancestor queries and level ancestor queries with respect to arbitrary roots in O(log n) time. Finally, with marked and unmarked vertices, we show how to compute distances to a nearest marked vertex. The later has applications to approximate nearest marked vertex in general graphs, and thereby to static optimization problems over shortest path metrics. Technically speaking, top trees can easily be derived from either Frederickson’s topology trees [Ambivalent Data Structures for Dynamic 2EdgeConnectivity and k Smallest Spanning Trees, SIAM J. Comput. 26 (2) pp. 484–538, 1997] or Sleator and Tarjan’s dynamic trees [A Data Structure for Dynamic Trees. J. Comput. Syst. Sc. 26
Planarization of Graphs Embedded on Surfaces
 in WG
, 1995
"... A planarizing set of a graph is a set of edges or vertices whose removal leaves a planar graph. It is shown that, if G is an nvertex graph of maximum degree d and orientable genus g, then there exists a planarizing set of O( p dgn) edges. This result is tight within a constant factor. Similar res ..."
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Cited by 9 (1 self)
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A planarizing set of a graph is a set of edges or vertices whose removal leaves a planar graph. It is shown that, if G is an nvertex graph of maximum degree d and orientable genus g, then there exists a planarizing set of O( p dgn) edges. This result is tight within a constant factor. Similar results are obtained for planarizing vertex sets and for graphs embedded on nonorientable surfaces. Planarizing edge and vertex sets can be found in O(n + g) time, if an embedding of G on a surface of genus g is given. We also construct an approximation algorithm that finds an O( p gn log g) planarizing vertex set of G in O(n log g) time if no genusg embedding is given as an input. 1 Introduction A graph G is planar if G can be drawn in the plane so that no two edges intersect. Planar graphs arise naturally in many applications of graph theory, e.g. in VLSI and circuit design, in network design and analysis, in computer graphics, and is one of the most intensively studied class of graphs [2...
Certificates and Fast Algorithms for Biconnectivity in FullyDynamic Graphs
 Third Annual European Symposium on Algorithms (ESA`95
, 1997
"... In this paper, we present sparse certificates for biconnectivity together with algorithms for updating these certificates. We thus obtain fullydynamic algorithms for biconnectivity in graphs that run in O( # n log n log# m n #) amortized time per operation, where m is the number of edges and n i ..."
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Cited by 8 (1 self)
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In this paper, we present sparse certificates for biconnectivity together with algorithms for updating these certificates. We thus obtain fullydynamic algorithms for biconnectivity in graphs that run in O( # n log n log# m n #) amortized time per operation, where m is the number of edges and n is the number of nodes in the graph. This improves upon the results in [12], in which algorithms were presented running in O( # m log n) amortized time, and solves the open problem to find certificates to speed up biconnectivity, as stated in [2]. 1 Introduction The field of dynamic graph algorithms has become an important field in algorithmic research in recent years. Currently, several results exist for incremental and fullydynamic graph problems, like for maintaining spanning trees, the 2edge or the 2vertexconnected components of a graph, or the planarity of a graph under the insertions and/or deletions of edges and vertices [3, 4, 5, 7, 8, 9, 10, 11, 12, 14]. In [4, 5, 12], algorith...
OnLine Convex Planarity Testing
, 1995
"... An important class of planar straightline drawings of graphs are the convex drawings, in which all faces are drawn as convex polygons. A graph is said to be convex planar if it admits a convex drawing. We consider the problem of testing convex planarity in a semidynamic environment, where a graph i ..."
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Cited by 6 (2 self)
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An important class of planar straightline drawings of graphs are the convex drawings, in which all faces are drawn as convex polygons. A graph is said to be convex planar if it admits a convex drawing. We consider the problem of testing convex planarity in a semidynamic environment, where a graph is subject to online insertions of vertices and edges. We present online algorithms for convex planarity testing with the following performance, where t denotes the number of vertices of the graph: convex planarity testing and insertion of vertices take 0(1) worstcase tinhe, insertion of edges takes 0(log n) amortized tinhe, and the space requirement of the data structure is O(n). Furthermore, we give a new combinatorial characterization of convex planar graphs.
A linear algorithm for finding a maximal planar subgraph
 SIAM J. Disc. Math
, 2006
"... Abstract. We construct an optimal lineartime algorithm for the maximal planar subgraph problem: given a graph G, find a planar subgraph G ′ of G such that adding to G ′ an extra edge of G results in a nonplanar graph. Our solution is based on a fast data structure for incremental planarity testing ..."
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Cited by 4 (0 self)
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Abstract. We construct an optimal lineartime algorithm for the maximal planar subgraph problem: given a graph G, find a planar subgraph G ′ of G such that adding to G ′ an extra edge of G results in a nonplanar graph. Our solution is based on a fast data structure for incremental planarity testing of triconnected graphs and a dynamic graph search procedure. Our algorithm can be transformed into a new optimal planarity testing algorithm. Key words. Planar graphs, planarity testing, incremental algorithms, graph planarization, data structures, triconnectivity. AMS subject classifications. 05C10, 05C85, 68R10, 68Q25, 68W40 1. Introduction. Agraphisplanar
Incremental Convex Planarity Testing
, 2001
"... An important class of planar straightline drawings of graphs are convex drawings, in which all the faces are drawn as convex polygons. A planar graph is said to be convex planar if it admits a convex drawing. We give a new combinatorial characterization of convex planar graphs based on the decompos ..."
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Cited by 1 (1 self)
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An important class of planar straightline drawings of graphs are convex drawings, in which all the faces are drawn as convex polygons. A planar graph is said to be convex planar if it admits a convex drawing. We give a new combinatorial characterization of convex planar graphs based on the decomposition of a biconnected graph into its triconnected components. We then consider the problem of testing convex planarity in an incremental environment, where a biconnected planar graph is subject to online insertions of vertices and edges. We present a data structure for the online incremental convex planarity testing problem with the following performance, where n denotes the current number of vertices of the graph: (strictly) convex planarity testing takes O(1) worstcase time, insertion of vertices takes O(log n) worstcase time, insertion of edges takes O(log n) amortized time, and the space requirement of the data structure is O(n).
Solving the Graph Planarization Problem Using an Improved Genetic Algorithm
, 2006
"... An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of ..."
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An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.