Journal of complex networks最新文献_第3页

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

4区数学

Journal of complex networks Pub Date : 2023-06-23 DOI: 10.1093/comnet/cnad028

Bogumił Kamiński, Paweł Prałat, François Théberge

引用次数: 0

PageRank centrality with non-local random walk-based teleportation 基于非局部随机移动的PageRank中心性

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-06-23 DOI: 10.1093/comnet/cnad024

David Bowater, E. Stefanakis

引用次数: 0

A multi-level generative framework for community detection in attributed networks 属性网络中社区检测的多级生成框架

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-04-21 DOI: 10.1093/comnet/cnad020

Yimei Zheng, Caiyan Jia, Xuanya Li

{"title":"A multi-level generative framework for community detection in attributed networks","authors":"Yimei Zheng, Caiyan Jia, Xuanya Li","doi":"10.1093/comnet/cnad020","DOIUrl":"https://doi.org/10.1093/comnet/cnad020","url":null,"abstract":"\u0000 Community detection in attributed networks is one of the most important tasks in complex network analysis. Many existing methods propose to integrate the network topology and node attribute from a generative aspect, which models an attributed network as a probabilistic generation process with the community distribution described by hidden variables. Though they can provide good interpretability to the community structure, it is difficult to infer community membership quickly due to their high computational complexity when inferring. Motivated by the multi-level strategy, in this study, we propose a multi-level generative framework to reduce the time cost of generative models for community detection in attributed networks. We first coarsen an attributed network into smaller ones by node matching. Then, we employ the existing generative model on the coarsest network without any modification for community detection, thus efficiently obtaining community memberships of nodes in this small coarsest network. Last, we project the assignments back to the original network through a local refinement mechanism to get communities. Extensive experiments on several real-world and artificial attributed networks show that our multi-level-based method is significantly faster than original generative models and is able to achieve better or more competitive results.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"18 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89377114","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

Information-based estimation of causality networks from high-dimensional multivariate time series 基于信息的高维多元时间序列因果网络估计

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-04-21 DOI: 10.1093/comnet/cnad015

Akylas Fotiadis, D. Kugiumtzis

{"title":"Information-based estimation of causality networks from high-dimensional multivariate time series","authors":"Akylas Fotiadis, D. Kugiumtzis","doi":"10.1093/comnet/cnad015","DOIUrl":"https://doi.org/10.1093/comnet/cnad015","url":null,"abstract":"\u0000 One of the most challenging aspects in the study of the complex dynamical systems is the estimation of their underlying, interdependence structure. Being in the era of Big Data, this problem gets even more complicated since more observed variables are available. To estimate direct causality effects in this setting, dimension reduction has to be employed in the Granger causality measure. The measure should also be capable to detect non-linear effects, persistently present in real-world complex systems. The model-free information-based measure of partial mutual information from mixed embedding (PMIME) has been developed to address these issues and it was found to perform well on multivariate time series of moderately high dimension. Here, the problem of forming complex networks from direct, possibly non-linear, high-dimensional time series at the order of hundreds is investigated. The performance of the measure PMIME is tested on two coupled dynamical systems in discrete time (coupled Hénon maps) and continuous time (coupled Mackey–Glass delay differential equations). It is concluded that the correct detection of the underlying causality network depends mainly on the network density rather than on its size (number of nodes). Finally, the effect of network size is investigated in the study of the British stock market in the period around Brexit.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"63 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80788689","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

Network of compression networks to extract useful information from multivariate time series 网络压缩网络从多变量时间序列中提取有用信息

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-04-21 DOI: 10.1093/comnet/cnad018

David M Walker, Débora C. Corrêa

引用次数: 1

Neuronal motifs reveal backbone structure and influential neurons of neural network in C. elegans 神经元基序揭示了秀丽隐杆线虫神经网络的骨干结构和有影响的神经元

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-04-21 DOI: 10.1093/comnet/cnad013

Jian Liu, Ye Yuan, Peng Zhao, Xiao Gu, H. Huo, Zhaoyu Li, T. Fang

{"title":"Neuronal motifs reveal backbone structure and influential neurons of neural network in C. elegans","authors":"Jian Liu, Ye Yuan, Peng Zhao, Xiao Gu, H. Huo, Zhaoyu Li, T. Fang","doi":"10.1093/comnet/cnad013","DOIUrl":"https://doi.org/10.1093/comnet/cnad013","url":null,"abstract":"\u0000 Neural network elements such as motif, backbone and influential nodes play important roles in neural network computation. Increasing researches have been applying complex network methods in order to identify different essential structures within complex neural networks. However, the distinct properties of synapses that build the neural network are often neglected, such as the difference between chemical synapses and electrical synapses. By separating these distinct synapses, we can identify a novel repertoire of neural motifs and greatly expand neural motif families in neural systems. Based on the expanded motif families, we further propose a novel neural-motif-based algorithm to extract the backbone in the neural network. The backbone circuit we extracted from Caenorhabditis elegans connectome controls an essential motor behaviour in C. elegans. Furthermore, we develop a novel neural-motif-based algorithm to identify influential neurons. Compared with the influential neurons identified using existing methods, the neurons identified in this work provide more information in related to their functions. These methods have been successfully applied to identify a series of network features in C. elegans, providing a biologically interpretable way of exploring the structure of neural network.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"283 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76838282","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

Correction to: Configuration models of random hypergraphs 修正:随机超图的配置模型

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-04-21 DOI: 10.1093/comnet/cnad014

引用次数: 0

A distributed adaptive routing against selective forwarding attack in scale-free network considering cascading failure 考虑级联故障的无标度网络中抗选择性转发攻击的分布式自适应路由

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-04-21 DOI: 10.1093/comnet/cnad021

Rong-rong Yin, Xuyao Ma, Huaili Yuan, Mengfa Zhai, Changjiang Guo

{"title":"A distributed adaptive routing against selective forwarding attack in scale-free network considering cascading failure","authors":"Rong-rong Yin, Xuyao Ma, Huaili Yuan, Mengfa Zhai, Changjiang Guo","doi":"10.1093/comnet/cnad021","DOIUrl":"https://doi.org/10.1093/comnet/cnad021","url":null,"abstract":"\u0000 To address the issues of data insecurity and unreliable transmission, redundancy-based data recovery can guarantee data security, but the increase of redundant data will reduce the robustness of network in the face of cascading failures. A distributed adaptive routing method in scale-free network is proposed to improve network resilience against selective forwarding attacks and the robustness against cascading failures. Based on the polynomial principle, the proposed routing method slices packets, adds redundancy reasonably and adopts multipath sequential routing method to completely send data to the destination node. The ability to resist selective forwarding attacks and robustness against cascading failures is investigated and analysed throughout the entire network operation. Simulation results show that our proposed routing method is not restricted by the number of disjoint paths, can maintain a higher data recovery ratio and resist effectively selective forwarding attacks, and also balances the network load well. Moreover, this routing method has a lower end-to-end latency for data transmission and is highly resistant to cascading failures under random and intentional attacks.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89885105","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

Worst case tractability of linear problems in the presence of noise: linear information 存在噪声的线性问题的最坏情况可跟踪性:线性信息

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-03-29 DOI: 10.48550/arXiv.2303.16328

L. Plaskota, Pawe l Siedlecki

引用次数: 0

Hypergraphx: a library for higher-order network analysis Hypergraphx:用于高阶网络分析的库

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-03-27 DOI: 10.1093/comnet/cnad019

Q. F. Lotito, Martina Contisciani, C. D. Bacco, Leonardo Di Gaetano, L. Gallo, A. Montresor, F. Musciotto, Nicolò Ruggeri, F. Battiston

{"title":"Hypergraphx: a library for higher-order network analysis","authors":"Q. F. Lotito, Martina Contisciani, C. D. Bacco, Leonardo Di Gaetano, L. Gallo, A. Montresor, F. Musciotto, Nicolò Ruggeri, F. Battiston","doi":"10.1093/comnet/cnad019","DOIUrl":"https://doi.org/10.1093/comnet/cnad019","url":null,"abstract":"\u0000 From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where hyperedges encode interactions among an arbitrary number of units. Here, we present an open-source python library, hypergraphx (HGX), providing a comprehensive collection of algorithms and functions for the analysis of higher-order networks. These include different ways to convert data across distinct higher-order representations, a large variety of measures of higher-order organization at the local and the mesoscale, statistical filters to sparsify higher-order data, a wide array of static and dynamic generative models, and an implementation of different dynamical processes with higher-order interactions. Our computational framework is general, and allows to analyse hypergraphs with weighted, directed, signed, temporal and multiplex group interactions. We provide visual insights on higher-order data through a variety of different visualization tools. We accompany our code with an extended higher-order data repository and demonstrate the ability of HGX to analyse real-world systems through a systematic analysis of a social network with higher-order interactions. The library is conceived as an evolving, community-based effort, which will further extend its functionalities over the years. Our software is available at https://github.com/HGX-Team/hypergraphx.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"13 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83427853","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: 8