On agglomeration-based rupture degree in networks and a heuristic algorithm

IF 0.3 Q4 COMPUTER SCIENCE, THEORY & METHODS Acta Universitatis Sapientiae Informatica Pub Date : 2023-08-01 DOI:10.2478/ausi-2023-0010
Muammer Ağtaş, T. Turacı
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引用次数: 0

Abstract

Abstract The rupture degree is one the most important vulnerability parameter in networks which are modelled by graphs. Let G(V (G),E (G)) be a simple undirected graph. The rupture degree is defined by r(G) = max{w(G–S )–|S |–m(G–S ):S ⊂ V (G) and w(G–S )>1} where m(G–S ) is the order of a largest connected component in G–S and w(G–S ) is the number of components of G–S, respectively. In this paper, we consider the vertex contraction method based on the network agglomeration operation for each vertex of G. Then, we have presented two graph vulnerability parameters called by agglomeration rupture degree and average lower agglomeration rupture degree. Furthermore, the exact values of them for some graph families are given. Finally, we proposed a polynomial time heuristic algorithm to obtain the values of agglomeration rupture degree and average
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基于团簇的网络破裂度及启发式算法
摘要破裂度是网络中最重要的脆弱性参数之一,网络是用图来建模的。设G(V (G),E (G))是一个简单无向图。断裂度定义为r(G) = max{w(G - S) - |S | - m(G - S):S∧V (G)和w(G - S)>1},其中m(G - S)为G - S中最大连通分量的阶数,w(G - S)为G - S的分量个数。本文考虑基于g的每个顶点的网络团聚操作的顶点收缩方法,给出了两个图脆弱性参数,分别称为团聚破裂度和平均较低团聚破裂度。进一步给出了某些图族的精确值。最后,我们提出了一种多项式时间启发式算法来获得团聚破裂程度和平均值
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来源期刊
Acta Universitatis Sapientiae Informatica
Acta Universitatis Sapientiae Informatica COMPUTER SCIENCE, THEORY & METHODS-
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