Journal of complex networks最新文献

Exact determination of MFPT for random walks on rounded fractal networks with varying topologies 精确确定拓扑结构不同的圆形分形网络上随机行走的 MFPT

IF 2.1 4区数学

Journal of complex networks Pub Date : 2024-05-22 DOI: 10.1093/comnet/cnae020

Yuanyuan Liu, Jing Chen, Weigang Sun

引用次数: 0

Flexible Bayesian inference on partially observed epidemics. 对部分观察到的流行病进行灵活的贝叶斯推断。

IF 2.1 4区数学

Journal of complex networks Pub Date : 2024-03-25 eCollection Date: 2024-04-01 DOI: 10.1093/comnet/cnae017

Maxwell H Wang, Jukka-Pekka Onnela

{"title":"Flexible Bayesian inference on partially observed epidemics.","authors":"Maxwell H Wang, Jukka-Pekka Onnela","doi":"10.1093/comnet/cnae017","DOIUrl":"10.1093/comnet/cnae017","url":null,"abstract":"Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this article, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC, which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioural change after positive tests or false test results.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10962317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140293592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An iterative spectral algorithm for digraph clustering 数图聚类的迭代光谱算法

IF 2.1 4区数学

Journal of complex networks Pub Date : 2024-02-21 DOI: 10.1093/comnet/cnae016

James Martin, Tim Rogers, Luca Zanetti

{"title":"An iterative spectral algorithm for digraph clustering","authors":"James Martin, Tim Rogers, Luca Zanetti","doi":"10.1093/comnet/cnae016","DOIUrl":"https://doi.org/10.1093/comnet/cnae016","url":null,"abstract":"\u0000 Graph clustering is a fundamental technique in data analysis with applications in many different fields. While there is a large body of work on clustering undirected graphs, the problem of clustering directed graphs is much less understood. The analysis is more complex in the directed graph case for two reasons: the clustering must preserve directional information in the relationships between clusters, and directed graphs have non-Hermitian adjacency matrices whose properties are less conducive to traditional spectral methods. Here, we consider the problem of partitioning the vertex set of a directed graph into k≥2 clusters so that edges between different clusters tend to follow the same direction. We present an iterative algorithm based on spectral methods applied to new Hermitian representations of directed graphs. Our algorithm performs favourably against the state-of-the-art, both on synthetic and real-world data sets. Additionally, it can identify a ‘meta-graph’ of k vertices that represents the higher-order relations between clusters in a directed graph. We showcase this capability on data sets about food webs, biological neural networks, and the online card game Hearthstone.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442744","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

Dynamic identification of important nodes in complex networks by considering local and global characteristics 考虑局部和全局特征，动态识别复杂网络中的重要节点

IF 2.1 4区数学

Journal of complex networks Pub Date : 2024-02-21 DOI: 10.1093/comnet/cnae015

Mengchuan Cao, Dan Wu, Pengxuan Du, Ting Zhang, Sina Ahmadi

{"title":"Dynamic identification of important nodes in complex networks by considering local and global characteristics","authors":"Mengchuan Cao, Dan Wu, Pengxuan Du, Ting Zhang, Sina Ahmadi","doi":"10.1093/comnet/cnae015","DOIUrl":"https://doi.org/10.1093/comnet/cnae015","url":null,"abstract":"\u0000 By combining centrality measures and community detection, a better insight into the nature of the evolution of important nodes in complex networks is obtained. Meanwhile, the dynamic identification of important nodes in complex networks can be enhanced by considering both local and global characteristics. Local characteristics focus on the immediate connections and interactions of a node within its neighbourhood, while global characteristics take into account the overall structure and dynamics of the entire network. Nodes with high local centrality in dynamic networks may play crucial roles in local information spreading or influence. On the global level, community detection algorithms have a significant impact on the overall network structure and connectivity between important nodes. Hence, integrating both local and global characteristics offers a more comprehensive understanding of how nodes dynamically contribute to the functioning of complex networks. For more comprehensive analysis of complex networks, this article identifies important nodes by considering local and global characteristics (INLGC). For local characteristic, INLGC develops a centrality measure based on network constraint coefficient, which can provide a better understanding of the relationship between neighbouring nodes. For global characteristic, INLGC develops a community detection method to improve the resolution of ranking important nodes. Extensive experiments have been conducted on several real-world datasets and various performance metrics have been evaluated based on the susceptible–infected–recovered model. The simulation results show that INLGC provides more competitive advantages in precision and resolution.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442319","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: Emergence of dense scale-free networks and simplicial complexes by random degree-copying 更正：通过随机度数复制出现致密无标度网络和简单复合物

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-12-22 DOI: 10.1093/comnet/cnad049

引用次数: 0

A generating-function approach to modelling complex contagion on clustered networks with multi-type branching processes 具有多类型分支过程的群集网络上复杂传染模型的生成函数方法

4区数学

Journal of complex networks Pub Date : 2023-11-07 DOI: 10.1093/comnet/cnad042

Leah A Keating, James P Gleeson, David J P O’Sullivan

引用次数: 2

Robustness of edge-coupled interdependent networks with reinforced edges 带增强边的边耦合相互依赖网络的鲁棒性

4区数学

Journal of complex networks Pub Date : 2023-11-07 DOI: 10.1093/comnet/cnad040

Junjie Zhang, Caixia Liu, Shuxin Liu, Fei Pan, Weifei Zang

{"title":"Robustness of edge-coupled interdependent networks with reinforced edges","authors":"Junjie Zhang, Caixia Liu, Shuxin Liu, Fei Pan, Weifei Zang","doi":"10.1093/comnet/cnad040","DOIUrl":"https://doi.org/10.1093/comnet/cnad040","url":null,"abstract":"Abstract Previous studies on cascade failures in interdependent networks have mainly focused on node coupling relationships. However, in realistic scenarios, interactions often occur at the edges connecting nodes rather than at the nodes themselves, giving rise to edge-coupled interdependent networks. In this article, we extend the model of partially edge-coupled interdependent networks by introducing reinforced edges with a ratio of ρ. We analyse the formation of finite surviving components in edge-coupled networks, wherein the reinforced edges can function and support their neighbouring nodes to form functional components. To accomplish this, we develop a framework through a detailed mathematical derivation of the proposed model. We then investigate the critical value ρ* of the reinforced edge ratio that can change the phase transition type of the network. Our model is verified by theoretical analysis, simulation experiments and real network systems. The results show that the introduction of a small proportion of reinforced edges in the edge-coupled interdependent network can avoid the sudden collapse of the network and significantly improve the robustness of the network.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545512","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

The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights gnar -边缘模型:一种用于边权时变网络的自回归模型

4区数学

Journal of complex networks Pub Date : 2023-11-07 DOI: 10.1093/comnet/cnad039

Anastasia Mantziou, Mihai Cucuringu, Victor Meirinhos, Gesine Reinert

{"title":"The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights","authors":"Anastasia Mantziou, Mihai Cucuringu, Victor Meirinhos, Gesine Reinert","doi":"10.1093/comnet/cnad039","DOIUrl":"https://doi.org/10.1093/comnet/cnad039","url":null,"abstract":"Abstract In economic and financial applications, there is often the need for analysing multivariate time series, comprising of time series for a range of quantities. In some applications, such complex systems can be associated with some underlying network describing pairwise relationships among the quantities. Accounting for the underlying network structure for the analysis of this type of multivariate time series is required for assessing estimation error and can be particularly informative for forecasting. Our work is motivated by a dataset consisting of time series of industry-to-industry transactions. In this example, pairwise relationships between Standard Industrial Classification (SIC) codes can be represented using a network, with SIC codes as nodes and pairwise transactions between SIC codes as edges, while the observed time series of the amounts of the transactions for each pair of SIC codes can be regarded as time-varying weights on the edges. Inspired by Knight et al. (2020, J. Stat. Softw., 96, 1–36), we introduce the GNAR-edge model which allows modelling of multiple time series utilizing the network structure, assuming that each edge weight depends not only on its past values, but also on past values of its neighbouring edges, for a range of neighbourhood stages. The method is validated through simulations. Results from the implementation of the GNAR-edge model on the real industry-to-industry data show good fitting and predictive performance of the model. The predictive performance is improved when sparsifying the network using a lead–lag analysis and thresholding edges according to a lead–lag score.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545774","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

Framework for converting mechanistic network models to probabilistic models. 将机械网络模型转换为概率模型的框架。

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-10-20 eCollection Date: 2023-10-01 DOI: 10.1093/comnet/cnad034

Ravi Goyal, Victor De Gruttola, Jukka-Pekka Onnela

{"title":"Framework for converting mechanistic network models to probabilistic models.","authors":"Ravi Goyal, Victor De Gruttola, Jukka-Pekka Onnela","doi":"10.1093/comnet/cnad034","DOIUrl":"10.1093/comnet/cnad034","url":null,"abstract":"There are two prominent paradigms for the modelling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49690733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insights from exact social contagion dynamics on networks with higher-order structures 从具有高阶结构网络的精确社会传染动力学中获得启示

IF 2.1 4区数学

Journal of complex networks Pub Date : 2023-09-22 DOI: 10.1093/comnet/cnad044

István Kiss, Iacopo Iacopini, P'eter L. Simon, N. Georgiou

{"title":"Insights from exact social contagion dynamics on networks with higher-order structures","authors":"István Kiss, Iacopo Iacopini, P'eter L. Simon, N. Georgiou","doi":"10.1093/comnet/cnad044","DOIUrl":"https://doi.org/10.1093/comnet/cnad044","url":null,"abstract":"Recently, there has been an increasing interest in studying dynamical processes on networks exhibiting higher-order structures, such as simplicial complexes, where the dynamics acts above and beyond dyadic interactions. Using simulations or heuristically derived epidemic spreading models, it was shown that new phenomena can emerge, such as bi-stability/multistability. Here, we show that such new emerging phenomena do not require complex contact patterns, such as community structures, but naturally result from the higher-order contagion mechanisms. We show this by deriving an exact higher-order Susceptible-Infected-Susceptible model and its limiting mean-field equivalent for fully connected simplicial complexes. Going beyond previous results, we also give the global bifurcation picture for networks with 3- and 4-body interactions, with the latter allowing for two non-trivial stable endemic steady states. Differently from previous approaches, we are able to study systems featuring interactions of arbitrary order. In addition, we characterize the contributions from higher-order infections to the endemic equilibrium as perturbations of the pairwise baseline, finding that these diminish as the pairwise rate of infection increases. Our approach represents a first step towards a principled understanding of higher-order contagion processes beyond triads and opens up further directions for analytical investigations.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139337831","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