Journal of complex networks最新文献_第5页

Correction to: Structural analysis of water networks 修正:水网结构分析

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-02-01 DOI: 10.1093/comnet/cnad008

引用次数: 0

A method based on link prediction for identifying set of super-spreaders in complex networks 一种基于链路预测的复杂网络超传播者集识别方法

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-02-01 DOI: 10.1093/comnet/cnad007

Bayan Hosseini;Farshid Veisi;Amir Sheikhahmdi

{"title":"A method based on link prediction for identifying set of super-spreaders in complex networks","authors":"Bayan Hosseini;Farshid Veisi;Amir Sheikhahmdi","doi":"10.1093/comnet/cnad007","DOIUrl":"https://doi.org/10.1093/comnet/cnad007","url":null,"abstract":"Identifying a group of key nodes with enormous capability for spreading information to other network nodes is one of the favourable research topics in complex networks. In most existing methods, only the current status of the network is used for identifying and selecting the member of these groups. The main weakness of these methods is a lack of attention to the highly dynamic nature of complex networks and continuous changes in them in terms of creating and eliminating nodes and links. This matter makes the selected group have no proper performance in spreading information relative to other nodes. Therefore, this article presents a novel method for identifying spreader nodes and selecting a superior set from them. In the proposed method, the diffusion power of network nodes is calculated in the first step, and some are selected as influential nodes. In the following steps, it is tried to modify the list of selected nodes by predicting the network variation. Six datasets gathered from real-world networks are utilized for evaluation. The proposed method and other methods are tested to evaluate their spread of influence and time complexity. Results show that using the link prediction in the proposed method can enhance the spread of influence by the selected set compared to other methods so that the spread of influence in some datasets is more than 30\u0000<tex>$%$</tex>\u0000. On the other hand, the time complexity of the proposed method confirms its utility in very large networks.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"11 2","pages":"52508-52524"},"PeriodicalIF":2.1,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49918047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A machine-learning procedure to detect network attacks 检测网络攻击的机器学习程序

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-01-15 DOI: 10.1093/comnet/cnad017

Davide Coppes, P. Cermelli

{"title":"A machine-learning procedure to detect network attacks","authors":"Davide Coppes, P. Cermelli","doi":"10.1093/comnet/cnad017","DOIUrl":"https://doi.org/10.1093/comnet/cnad017","url":null,"abstract":"The goal of this note is to assess whether simple machine learning algorithms can be used to determine whether and how a given network has been attacked. The procedure is based on the $k$-Nearest Neighbor and the Random Forest classification schemes, using both intact and attacked ErdH{o}s-R'enyi, Barabasi-Albert and Watts-Strogatz networks to train the algorithm. The types of attacks we consider here are random failures and maximum-degree or maximum-betweenness node deletion. Each network is characterized by a list of 4 metrics, namely the normalized reciprocal maximum degree, the global clustering coefficient, the normalized average path length and the assortativity: a statistical analysis shows that this list of graph metrics is indeed significantly different in intact or damaged networks. We test the procedure by choosing both artificial and real networks, performing the attacks and applying the classification algorithms to the resulting graphs: the procedure discussed here turns out to be able to distinguish between intact networks and those attacked by the maximum-degree of maximum-betweenness deletions, but cannot detect random failures. Our results suggest that this approach may provide a basis for the analysis and detection of network attacks.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"30 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82823521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correlation distances in social networks 社交网络中的相关距离

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-12-21 DOI: 10.1093/comnet/cnad016

Pádraig MacCarron, Shane Mannion, T. Platini

引用次数: 0

A robust method for fitting degree distributions of complex networks 复杂网络度分布拟合的鲁棒方法

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-12-13 DOI: 10.1093/comnet/cnad023

Shane Mannion, Pádraig MacCarron

{"title":"A robust method for fitting degree distributions of complex networks","authors":"Shane Mannion, Pádraig MacCarron","doi":"10.1093/comnet/cnad023","DOIUrl":"https://doi.org/10.1093/comnet/cnad023","url":null,"abstract":"This work introduces a method for fitting to the degree distributions of complex network datasets, such that the most appropriate distribution from a set of candidate distributions is chosen while maximizing the portion of the distribution to which the model is fit. Current methods for fitting to degree distributions in the literature are inconsistent and often assume a priori what distribution the data are drawn from. Much focus is given to fitting to the tail of the distribution, while a large portion of the distribution below the tail is ignored. It is important to account for these low degree nodes, as they play crucial roles in processes such as percolation. Here we address these issues, using maximum likelihood estimators to fit to the entire dataset, or close to it. This methodology is applicable to any network dataset (or discrete empirical dataset), and we test it on over 25 network datasets from a wide range of sources, achieving good fits in all but a few cases. We also demonstrate that numerical maximization of the likelihood performs better than commonly used analytical approximations. In addition, we have made available a Python package which can be used to apply this methodology.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"73 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87624292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tractability of L2-approximation and integration in weighted Hermite spaces of finite smoothness 有限光滑加权Hermite空间中l2逼近与积分的可跟踪性

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-12-12 DOI: 10.48550/arXiv.2212.05780

G. Leobacher, F. Pillichshammer, Adrian Ebert

引用次数: 1

A cosine rule-based discrete sectional curvature for graphs 基于余弦规则的离散截面曲率图

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-12-02 DOI: 10.1093/comnet/cnad022

J. D. Plessis, X. Arsiwalla

{"title":"A cosine rule-based discrete sectional curvature for graphs","authors":"J. D. Plessis, X. Arsiwalla","doi":"10.1093/comnet/cnad022","DOIUrl":"https://doi.org/10.1093/comnet/cnad022","url":null,"abstract":"How does one generalize differential geometric constructs such as curvature of a manifold to the discrete world of graphs and other combinatorial structures? This problem carries significant importance for analyzing models of discrete spacetime in quantum gravity; inferring network geometry in network science; and manifold learning in data science. The key contribution of this paper is to introduce and validate a new estimator of discrete sectional curvature for random graphs with low metric-distortion. The latter are constructed via a specific graph sprinkling method on different manifolds with constant sectional curvature. We define a notion of metric distortion, which quantifies how well the graph metric approximates the metric of the underlying manifold. We show how graph sprinkling algorithms can be refined to produce hard annulus random geometric graphs with minimal metric distortion. We construct random geometric graphs for spheres, hyperbolic and euclidean planes; upon which we validate our curvature estimator. Numerical analysis reveals that the error of the estimated curvature diminishes as the mean metric distortion goes to zero, thus demonstrating convergence of the estimate. We also perform comparisons to other existing discrete curvature measures. Finally, we demonstrate two practical applications: (i) estimation of the earth's radius using geographical data; and (ii) sectional curvature distributions of self-similar fractals.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"14 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89522668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sampling numbers of smoothness classes via 𝓁1-minimization 通过𝓁1-minimization获取平滑度类的采样数

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-12-01 DOI: 10.48550/arXiv.2212.00445

Thomas Jahn, T. Ullrich, Felix Voigtländer

引用次数: 8

Improving mean-field network percolation models with neighbourhood information and their limitations on highly modular, highly dispersed networks 基于邻域信息的平均场网络渗透模型的改进及其在高度模块化、高度分散网络中的局限性

IF 2.1 4区数学

Journal of complex networks Pub Date : 2022-11-04 DOI: 10.48550/arXiv.2211.02346

Chris Jones, K. Wiesner

引用次数: 1

Hypergraph Artificial Benchmark for Community Detection (h-ABCD) 社区检测的超图人工基准