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首页> 外文期刊>Journal of Graph Algorithms and Applications >Maximal Neighborhood Search and Rigid Interval Graphs
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Maximal Neighborhood Search and Rigid Interval Graphs

机译:最大邻域搜索和刚性间隔图

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摘要

A rigid interval graph is an interval graph which has only one clique tree. In 2009, Panda and Das show that all connected unit interval graphs are rigid interval graphs. Generalizing the two classic graph search algorithms, Lexicographic Breadth-First Search ( LBFS ) and Maximum Cardinality Search ( MCS ), Corneil and Krueger propose in 2008 the so-called Maximal Neighborhood Search ( MNS ) and show that one sweep of MNS is enough to recognize chordal graphs. We develop the MNS properties of rigid interval graphs and characterize this graph class in several different ways. This allows us obtain several linear time multi-sweep MNS algorithms for recognizing rigid interval graphs and unit interval graphs, generalizing a corresponding 3-sweep LBFS algorithm for unit interval graph recognition designed by Corneil in 2004. For unit interval graphs, we even present a new linear time 2-sweep MNS certifying recognition algorithm.
机译:刚性间隔图是仅包含一个类树的间隔图。在2009年,Panda和Das显示所有连接的单位间隔图均为刚性间隔图。概括了两种经典的图搜索算法:词法广度优先搜索(LBFS)和最大基数搜索(MCS),Corneil和Krueger在2008年提出了所谓的最大邻域搜索(MNS),证明一次MNS扫描足以满足识别和弦图。我们开发了刚性区间图的MNS属性,并以几种不同的方式来表征该图类。这使我们获得了几种用于识别刚性间隔图和单位间隔图的线性时间多扫描MNS算法,归纳了Corneil在2004年设计的用于单位间隔图识别的相应3扫描LBFS算法。对于单位间隔图,我们甚至提出了新的线性时间2扫MNS认证识别算法。

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