Connectivity-preserving distributed algorithms for removing links in directed networks

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY

Network Science Pub Date : 2022-09-01 DOI:10.1017/nws.2022.25

Azwirman Gusrialdi

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引用次数: 0

Abstract

Abstract This article considers the link removal problem in a strongly connected directed network with the goal of minimizing the dominant eigenvalue of the network’s adjacency matrix while maintaining its strong connectivity. Due to the complexity of the problem, this article focuses on computing a suboptimal solution. Furthermore, it is assumed that the knowledge of the overall network topology is not available. This calls for distributed algorithms which rely solely on the local information available to each individual node and information exchange between each node and its neighbors. Two different strategies based on matrix perturbation analysis are presented, namely simultaneous and iterative link removal strategies. Key ingredients in implementing both strategies include novel distributed algorithms for estimating the dominant eigenvectors of an adjacency matrix and for verifying strong connectivity of a directed network under link removal. It is shown via numerical simulations on different type of networks that in general the iterative link removal strategy yields a better suboptimal solution. However, it comes at a price of higher communication cost in comparison to the simultaneous link removal strategy.

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有向网络中去除链路的保连通分布式算法

摘要本文考虑了一个强连接有向网络中的链路移除问题，其目标是在保持网络的强连通性的同时最小化网络邻接矩阵的优势特征值。由于问题的复杂性，本文着重于计算次优解决方案。此外，假设不具备整个网络拓扑的知识。这需要分布式算法，该算法仅依赖于每个单独节点可用的本地信息以及每个节点与其相邻节点之间的信息交换。提出了基于矩阵摄动分析的两种不同的链路移除策略，即同步移除策略和迭代移除策略。实现这两种策略的关键因素包括用于估计邻接矩阵的主要特征向量的新型分布式算法，以及用于验证链路移除下有向网络的强连通性。通过对不同类型网络的数值模拟表明，迭代链路移除策略通常产生较好的次优解。然而，与同时删除链路策略相比，它的通信成本更高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

3.50

自引率

5.90%

发文量

期刊介绍： Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.