Optimizing Multilayer Networks Through Time-Dependent Decision-Making: A Comparative Study.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2025-07-08 DOI:10.1089/big.2024.0094
Kenan Menguc, Alper Yilmaz
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引用次数: 0

Abstract

This research highlights the importance of accurately analyzing real-world multilayer network problems and introduces effective solutions. Whether simulating protein-protein network, transportation network, or a social network, representation and analysis over these networks are crucial. Multilayer networks, that contain added layers, may undergo dynamic transformations over time akin to single-layer networks that experience changes over time. These dynamic networks, that expand and contract, can be optimized by guidance from human operators if the transient changes are known and can be controlled. For the expansion and contraction of networks, this study introduces two distinct algorithms designed to make optimal decisions across dynamic changes of a multilayer network. The main strategy is to minimize the standard deviation across betweenness centrality of the edges in a complex network. The approaches we introduce incorporate diverse constraints into a multilayer weighted network, probing the network's expansion or contraction under various conditions represented as objective functions. The addition of changing of objective function enhances the model's adaptability to solve a wide array of problem types. In this way, complex network structures representing real-world problems can be mathematically modeled which makes it easier to make informed decisions.

基于时间依赖决策的多层网络优化:比较研究。
本研究强调了准确分析实际多层网络问题的重要性,并介绍了有效的解决方案。无论是模拟蛋白质-蛋白质网络、运输网络还是社会网络,对这些网络的表示和分析都是至关重要的。包含附加层的多层网络可能会随时间发生动态变化,类似于单层网络随时间发生变化。这些动态网络可以扩展和收缩,如果瞬态变化是已知的,并且可以控制,则可以通过人工操作人员的指导进行优化。对于网络的扩展和收缩,本研究引入了两种不同的算法,旨在跨多层网络的动态变化做出最优决策。其主要策略是最小化复杂网络中沿中间性和中心性的标准偏差。我们引入的方法将不同的约束纳入多层加权网络,探测网络在目标函数表示的各种条件下的扩张或收缩。目标函数变化的加入,增强了模型对广泛问题类型的适应性。通过这种方式,可以对代表现实世界问题的复杂网络结构进行数学建模,从而更容易做出明智的决策。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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