Complex networks are a powerful way to reason about systems with non-trivial patterns of interaction. The increased attention in this research area is accelerated by the increasing availability of complex network data sets, with data often being reused as secondary data sources. Typically, multiple data sources are combined to create a larger, fuller picture of these complex networks and in doing so scientists have to make sometimes subjective decisions about how these sources should be integrated. These seemingly trivial decisions can sometimes have significant impact on both the resultant integrated networks and any downstream network models executed on them. We highlight the importance of this impact in online social networks and dark networks, two use-cases where data are regularly combined from multiple sources due to challenges in measurement or overlap of networks. We present a method for systematically testing how different, realistic data integration approaches can alter both the networks themselves and network models run on them, as well as an associated Python package (NIDMod) that implements this method. A number of experiments show the effectiveness of our method in identifying the impact of different data integration setups on network diffusion models.
{"title":"An approach for analysing the impact of data integration on complex network diffusion models","authors":"J. Nevin, Paul Groth, M. Lees","doi":"10.1093/comnet/cnad025","DOIUrl":"https://doi.org/10.1093/comnet/cnad025","url":null,"abstract":"\u0000 Complex networks are a powerful way to reason about systems with non-trivial patterns of interaction. The increased attention in this research area is accelerated by the increasing availability of complex network data sets, with data often being reused as secondary data sources. Typically, multiple data sources are combined to create a larger, fuller picture of these complex networks and in doing so scientists have to make sometimes subjective decisions about how these sources should be integrated. These seemingly trivial decisions can sometimes have significant impact on both the resultant integrated networks and any downstream network models executed on them. We highlight the importance of this impact in online social networks and dark networks, two use-cases where data are regularly combined from multiple sources due to challenges in measurement or overlap of networks. We present a method for systematically testing how different, realistic data integration approaches can alter both the networks themselves and network models run on them, as well as an associated Python package (NIDMod) that implements this method. A number of experiments show the effectiveness of our method in identifying the impact of different data integration setups on network diffusion models.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74704046","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}
Complex networks can often exhibit a high degree of bipartivity. There are many well-known ways for testing this, and in this article, we give a systematic analysis of characterizations based on the spectra of the adjacency matrix and various graph Laplacians. We show that measures based on these characterizations can be drastically different results and leads us to distinguish between local and global loss of bipartivity. We test several methods for finding approximate bipartitions based on analysing eigenvectors and show that several alternatives seem to work well (and can work better than more complex methods) when augmented with local improvement.
{"title":"Spectral techniques for measuring bipartivity and producing partitions","authors":"Azhar Aleidan, P. Knight","doi":"10.1093/comnet/cnad026","DOIUrl":"https://doi.org/10.1093/comnet/cnad026","url":null,"abstract":"\u0000 Complex networks can often exhibit a high degree of bipartivity. There are many well-known ways for testing this, and in this article, we give a systematic analysis of characterizations based on the spectra of the adjacency matrix and various graph Laplacians. We show that measures based on these characterizations can be drastically different results and leads us to distinguish between local and global loss of bipartivity. We test several methods for finding approximate bipartitions based on analysing eigenvectors and show that several alternatives seem to work well (and can work better than more complex methods) when augmented with local improvement.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84564844","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}
Abstract Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node’s local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model’s performance against the recently introduced message-passing models and provide evidence that the standard version is also outperformed, while the ‘loopy’ version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message-passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterized by high mixing times, identifying this as a general limitation of percolation prediction models.
{"title":"Improving mean-field network percolation models with neighbourhood information","authors":"Chris Jones, Karoline Wiesner","doi":"10.1093/comnet/cnad029","DOIUrl":"https://doi.org/10.1093/comnet/cnad029","url":null,"abstract":"Abstract Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node’s local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model’s performance against the recently introduced message-passing models and provide evidence that the standard version is also outperformed, while the ‘loopy’ version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message-passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterized by high mixing times, identifying this as a general limitation of percolation prediction models.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136085163","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}
Abstract The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known Lancichinetti, Fortunato, Radicchi (LFR) one, and its main parameter ξ can be tuned to mimic its counterpart in the LFR model, the mixing parameter μ. In this article, we introduce hypergraph counterpart of the ABCD model, h–ABCD, which also produces random hypergraph with distributions of ground-truth community sizes and degrees following power-law. As in the original ABCD, the new model h–ABCD can produce hypergraphs with various levels of noise. More importantly, the model is flexible and can mimic any desired level of homogeneity of hyperedges that fall into one community. As a result, it can be used as a suitable, synthetic playground for analyzing and tuning hypergraph community detection algorithms. [Received on 22 October 2022; editorial decision on 18 July 2023; accepted on 19 July 2023]
ABCD (Artificial Benchmark for Community Detection)图是近年来提出的一种随机图模型，它具有社团结构和社团大小的幂律分布。该模型生成的图与著名的Lancichinetti, Fortunato, Radicchi (LFR)模型具有相似的性质，并且其主要参数ξ可以被调整以模拟LFR模型中的对应参数，即混合参数μ。在本文中，我们引入了ABCD模型的对应超图h-ABCD, h-ABCD也产生了基于真值社区大小和度服从幂律分布的随机超图。与原来的ABCD一样，新模型h-ABCD可以产生具有不同程度噪声的超图。更重要的是，该模型是灵活的，可以模拟属于一个社区的任何期望级别的超边缘同质性。因此，它可以作为一个合适的综合平台，用于分析和调优超图社区检测算法。[2022年10月22日收到;2023年7月18日的编辑决定;于2023年7月19日接受]
{"title":"Hypergraph Artificial Benchmark for Community Detection (h–ABCD)","authors":"Bogumił Kamiński, Paweł Prałat, François Théberge","doi":"10.1093/comnet/cnad028","DOIUrl":"https://doi.org/10.1093/comnet/cnad028","url":null,"abstract":"Abstract The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known Lancichinetti, Fortunato, Radicchi (LFR) one, and its main parameter ξ can be tuned to mimic its counterpart in the LFR model, the mixing parameter μ. In this article, we introduce hypergraph counterpart of the ABCD model, h–ABCD, which also produces random hypergraph with distributions of ground-truth community sizes and degrees following power-law. As in the original ABCD, the new model h–ABCD can produce hypergraphs with various levels of noise. More importantly, the model is flexible and can mimic any desired level of homogeneity of hyperedges that fall into one community. As a result, it can be used as a suitable, synthetic playground for analyzing and tuning hypergraph community detection algorithms. [Received on 22 October 2022; editorial decision on 18 July 2023; accepted on 19 July 2023]","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136085164","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}
PageRank is a popular measure of centrality that is often applied to rank nodes in real-world networks. However, in many cases, the notion of teleportation is counterintuitive because it implies that whatever is moving around the network will jump or ‘teleport’ directly from one node to any other, without considering how far apart the nodes are. To overcome this issue, we propose here a general measure of PageRank centrality whereby the teleportation probabilities depend, in some way, on the distance separating the nodes. We accomplish this by drawing upon recent advances in non-local random walks, which allow the proposed measure to be tailored for various real-world networks and applications. To illustrate the flexibility of the proposed measure and to demonstrate how it differs from PageRank centrality, we present and discuss experimental results for a selection of real-world spatial and social networks, including an air transportation network, a collaboration network and an urban street network.
{"title":"PageRank centrality with non-local random walk-based teleportation","authors":"David Bowater, E. Stefanakis","doi":"10.1093/comnet/cnad024","DOIUrl":"https://doi.org/10.1093/comnet/cnad024","url":null,"abstract":"\u0000 PageRank is a popular measure of centrality that is often applied to rank nodes in real-world networks. However, in many cases, the notion of teleportation is counterintuitive because it implies that whatever is moving around the network will jump or ‘teleport’ directly from one node to any other, without considering how far apart the nodes are. To overcome this issue, we propose here a general measure of PageRank centrality whereby the teleportation probabilities depend, in some way, on the distance separating the nodes. We accomplish this by drawing upon recent advances in non-local random walks, which allow the proposed measure to be tailored for various real-world networks and applications. To illustrate the flexibility of the proposed measure and to demonstrate how it differs from PageRank centrality, we present and discuss experimental results for a selection of real-world spatial and social networks, including an air transportation network, a collaboration network and an urban street network.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89327526","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}
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.
{"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":null,"pages":null},"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}
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.
{"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":null,"pages":null},"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}
Compression networks are the result of a recently proposed method to transform univariate time series to a complex network representation by using a compression algorithm. We show how a network of compression networks can be constructed to capture relationships among multivariate time series. This network is a weighted graph with edge weights corresponding to how well the compression codewords of one time series compress another time series. Subgraphs of this network obtained by thresholding of the relative compression edge weights are shown to possess properties which can track dynamical change. Furthermore, community structures—groups of vertices more densely connected together—within these networks can identify partially synchronized states in the dynamics of networked oscillators, as well as perform genre classification of musical compositions. An additional example incorporates temporal windowing of the data and demonstrates the potential of the method to identify tipping point behaviour through the analysis of multivariate electroencephalogram time series of patients undergoing seizure.
{"title":"Network of compression networks to extract useful information from multivariate time series","authors":"David M Walker, Débora C. Corrêa","doi":"10.1093/comnet/cnad018","DOIUrl":"https://doi.org/10.1093/comnet/cnad018","url":null,"abstract":"\u0000 Compression networks are the result of a recently proposed method to transform univariate time series to a complex network representation by using a compression algorithm. We show how a network of compression networks can be constructed to capture relationships among multivariate time series. This network is a weighted graph with edge weights corresponding to how well the compression codewords of one time series compress another time series. Subgraphs of this network obtained by thresholding of the relative compression edge weights are shown to possess properties which can track dynamical change. Furthermore, community structures—groups of vertices more densely connected together—within these networks can identify partially synchronized states in the dynamics of networked oscillators, as well as perform genre classification of musical compositions. An additional example incorporates temporal windowing of the data and demonstrates the potential of the method to identify tipping point behaviour through the analysis of multivariate electroencephalogram time series of patients undergoing seizure.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87452417","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}
Jian Liu, Ye Yuan, Peng Zhao, Xiao Gu, H. Huo, Zhaoyu Li, T. Fang
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.
{"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":null,"pages":null},"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}
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.
{"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":null,"pages":null},"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}
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