Algorithm for Link Prediction in a Self-Regulating Network with Adaptive Topology Based on Graph Theory and Machine Learning

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
E. Yu. Pavlenko
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引用次数: 0

Abstract

This article presents a functional graph model of a network with adaptive topology, where the network nodes represent the graph vertices, and data exchange between the nodes is represented as edges. The dynamic nature of network interaction complicates the solution of the problem of monitoring and controlling the operation of a network with adaptive topology, which should be done in order to ensure a guaranteed correct network interaction. The importance of solving such a problem is justified by the creation of modern information and cyber-physical systems, which are based on networks with adaptive topology. The dynamic nature of links between nodes, on the one hand, makes it possible to provide self-regulation of the network and, on the other hand, significantly complicates the control over the network operation, because it is impossible to identify a single pattern of network interaction. On the basis of the developed model of the network with adaptive topology, a graph algorithm for link prediction is proposed, which is extended to the case of peer-to-peer networks. The algorithm is based on the significant parameters of network nodes characterizing both their physical characteristics (signal level and battery charge) and their characteristics as objects of network interaction (characteristics of the centrality of graph nodes). The correctness and adequacy of the developed algorithm is confirmed by the experimental results on modeling a peer-to-peer network with adaptive topology and its self-regulation when different nodes are removed.

Abstract Image

基于图论和机器学习的自适应拓扑自调节网络链路预测算法
本文提出了一种自适应拓扑网络的功能图模型,其中网络节点表示图顶点,节点之间的数据交换表示为边。网络交互的动态性使自适应拓扑网络的运行监控问题的解决变得复杂,而监控是保证正确的网络交互的必要条件。现代信息和网络物理系统的创建证明了解决这一问题的重要性,这些系统基于具有自适应拓扑结构的网络。节点之间链接的动态性一方面使网络的自我调节成为可能,另一方面也使对网络运行的控制变得非常复杂,因为不可能确定网络交互的单一模式。在建立自适应拓扑网络模型的基础上,提出了一种链路预测的图算法,并将其推广到对等网络中。该算法基于网络节点的重要参数,这些参数既表征了网络节点的物理特征(信号水平和电池电量),也表征了网络交互对象的特征(图节点的中心性特征)。通过对具有自适应拓扑结构的点对点网络进行建模,并在不同节点被移除时进行自调节,实验结果验证了该算法的正确性和充分性。
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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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