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Detecting Critical Nodes In Sparse Graphs

机译:在稀疏图中检测关键节点

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Identifying critical nodes in a graph is important to understand the structural characteristics and the connectivity properties of the network. In this paper, we focus on detecting critical nodes, or nodes whose deletion results in the minimum pair-wise connectivity among the remaining nodes. This problem, known as the CRITICAL node problem has applications in several fields including biomedicine, telecommunications, and military strategic planning. We show that the recognition version of the problem NP-is complete and derive a mathematical formulation based on integer linear programming. In addition, we propose a heuristic for the problem which exploits the combinatorial structure of the graph. The heuristic is then enhanced by the application of a local improvement method. A computational study is presented in which we apply the integer programming formulation and the heuristic to real and randomly generated data sets. For all instances tested, the heuristic is able to efficiently provide optimal solutions in a fraction of the time required by a commercial software package.
机译:识别图中的关键节点对于理解网络的结构特征和连接属性很重要。在本文中,我们专注于检测关键节点或删除其节点会导致其余节点之间的最小成对连接性的节点。该问题被称为“关键节点问题”,已在包括生物医学,电信和军事战略规划在内的多个领域中得到应用。我们证明问题NP-的识别版本是完整的,并基于整数线性规划推导了数学公式。此外,我们针对该问题提出了一种启发式方法,该方法利用了图的组合结构。然后通过应用局部改进方法来增强启发式方法。提出了计算研究,其中我们将整数编程公式和启发式方法应用于实际和随机生成的数据集。对于所有测试的实例,启发式方法能够在商业软件包所需的一小部分时间内有效地提供最佳解决方案。

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