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  • 有关BP神经网络的一些论文,例如:基于BP算法的模糊神经网络的研究
  • 这里有很多有用的论文,欢迎来下载!我愿意与大家一起分享,快快行动吧!
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  • FLAX_ Systematic Discovery of Client-side Validation Vulnerabilities in Rich Web Applications.pdf
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  • 我已阅读的有关机器学习和复杂网络的书籍,论文。 1.(1)(WS网络)的集体动力学(2)爆裂和沉重的起因(3)复杂系统中的突发性和内存(4)人类交流动态中的呼叫方式(5)人群对大规模突发事件的集体React(6)...
  • 该知识库是对SIGCSE 2015上发表的论文的补充,可。 实验室 在labs /文件夹下有一部分实验分配。 某些作业(例如,道德操守实验室)被排除在外,因为它们需要根据给出的国家/大学的规则进行重大定制,或者可能需要...
  • 这是我当时写的有关网络安全的论文,到现在看仍然是比较好的观点
  • 最近阅读了学术论文有关Web安全性/模糊性文章等,以及一些由我本人撰写或从其他来源摘录阅读笔记。 目录 推荐会议 会议 全名 dblp链接 CCS ACM计算机和通信安全会议 Usenix USENIX安全研讨会 标普 IEEE...
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  • 请访问项目以获取有关本文和总体方法更多详细信息。 下载及安装 使用以下命令来获取此代码存储库。 使用--recursive选项来获取此代码使用git子模块。 git clone --recursive ...
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  • 本文范围更广,因为它试图根据“争端”来探索与计算机网络攻击/操作有关的问题,并提出有关此问题新趋势,而各​​州和国际组织已经在解决这一趋势,即北约。 该分析考虑了战时强制法和编纂过现行法律,其...
  • 网络五子棋 论文

    2014-05-29 16:14:45
    有关c++网络五子棋设计的论文 里面有源代码及软件 开发工具是vc6.0
  • 无限宽度神经网络是近来一个重要研究课题,但要通过实证实验来探索它们性质,必需大规模计算能力才行。近日,谷歌大脑公布一篇论文介绍了他们在有限和无限神经网络方面系统性探索成果。该研...

    无限宽度神经网络是近来一个重要的研究课题,但要通过实证实验来探索它们的性质,必需大规模的计算能力才行。近日,谷歌大脑公布的一篇论文介绍了他们在有限和无限神经网络方面的系统性探索成果。该研究通过大规模对比实验得到了 12 条重要的实验结论并在此过程中找到了一些新的改进方法。该文作者之一 Jascha Sohl-Dickstein 表示:「这篇论文包含你想知道的但没有足够的计算能力探求的有关无限宽度网络的一切!

    >>>>

    近日,谷歌大脑的研究者通过大规模实证研究探讨了宽神经网络与核(kernel)方法之间的对应关系。在此过程中,研究者解决了一系列与无限宽度神经网络研究相关的问题,并总结得到了 12 项实验结果。

    此外,实验还额外为权重衰减找到了一种改进版逐层扩展方法,可以提升有限宽度网络的泛化能力。

    最后,他们还为使用 NNGP(神经网络高斯过程)和 NT(神经正切)核的预测任务找到了一种改进版的最佳实践,其中包括一种全新的集成(ensembling)技术。这些最佳实践技术让实验中每种架构对应的核在 CIFAR-10 分类任务上均取得了当前最佳的成绩。

    论文链接:https://arxiv.org/pdf/2007.15801v1.pdf

    当使用贝叶斯方法和梯度下降方法训练的神经网络的中间层是无限宽时,这些网络可以收敛至高斯过程或紧密相关的核方法。这些无限宽度网络的预测过程可通过贝叶斯网络的神经网络高斯过程(NNGP)核函数来描述,也可通过梯度下降方法所训练网络的神经正切核(NTK)和权重空间线性化来描述。

    这种对应关系是近来在理解神经网络方面获得突破的关键,同时还使核方法、贝叶斯深度学习、主动学习和半监督学习取得了切实的进步。在为大规模神经网络提供确切理论描述时,NNGP、NTK 和相关的宽度限制都是独特的。因此可以相信它们仍将继续为深度学习理论带来变革。

    无限网络是近来一个活跃的研究领域,但其基础性的实证问题仍待解答。谷歌大脑的这项研究对有限和无限宽度神经网络进行了广泛深入的实证研究。在此过程中,研究者通过实证数据定量地解答了影响有限网络和核方法性能的变化因素,揭示了出人意料的新行为,并开发了可提升有限与无限宽度网络性能的最佳实践。

    实验设计

    为了系统性地对无限和有限神经网络进行实证研究,研究者首先确立了每种架构的 base,方便直接对比无限宽度核方法、线性化权重空间网络和基于非线性梯度下降的训练方法。对于有限宽度的情况,base 架构使用了恒定小学习率且损失为 MSE(均方误差)的 mini-batch 梯度下降。在核学习设置中,研究者为整个数据集计算了 NNGP 和 NTK。

    完成这种一对一的比较之后,研究者在 base 模型之上进行了大量不同种类的修改。某些修改会大致保留其对应关系(比如数据增强),而另一些则会打破这种对应关系,并且假设对应关系的打破会影响到性能结果(比如使用较大的学习率)。

    此外,研究者还围绕 base 模型的初始化对其进行线性化尝试,在这种情况下,其训练动态可使用常量核来精准地描述。由于有限宽度效应,这不同于前文描述的核设置。

    该研究使用 MSE 损失的原因是能更容易地与核方法进行比较,交叉熵损失在性能方面比 MSE 损失略好,但这还留待未来研究。

    该研究涉及的架构要么是基于全连接层(FCN)构建的,要么就是用卷积层(CNN)构建的。所有案例都使用了 ReLU 非线性函数。除非另有说明,该研究使用的模型都是 3 层的 FCN 和 8 层的 CNN。对于卷积网络,在最后的读出层(readout layer)之前必须压缩图像形状数据的空间维度。为此,要么是将图像展平为一维向量(VEC),要么是对空间维度应用全局平均池化(GAP)。

    最后,研究者比较了两种参数化网络权重和偏置的方法:标准参数化(STD)和 NTK 参数化(NTK)。其中 STD 用于有限宽度网络的研究,NTK 则在目前大多数无限宽度网络研究中得到应用。

    除非另有说明,该研究中所有核方法的实验都是基于对角核正则化(diagonal kernel regularization)独立优化完成的。有限宽度网络则全都使用了与 base 模型相对应的小学习率。

    这篇论文中的实验基本都是计算密集型的。举个例子,要为 CNN-GAP 架构在 CIFAR-10 上计算 NTK 或 NNGP,就必须用 6×10^7 乘 6×10^7 的核矩阵对各项进行评估。通常来说,这需要双精度 GPU 时间约 1200 小时,因此研究者使用了基于 beam 的大规模分布式计算基础设施。

    所有实验都使用了基于 JAX 的 Neural Tangents 库:https://github.com/google/neural-tangents。

    为了尽可能地做到系统性,同时又考虑到如此巨大的计算需求,于是研究者仅使用了一个数据集 CIFAR-10,即在该数据集上评估对每种架构的每种修改措施。同时,为了保证结果也适用于不同的数据集,研究者还在 CIFAR-100 和 Fashion-MNIST 上评估了部分关键结果。

    从实验中得到的 12 条结论

    以下为基于实验结果总结的 12 个结论(详细分析请参阅原论文):

    1. NNGP/NTK 的表现可胜过有限网络

    在无限网络研究中,一个常见假设是它们在大数据环境中的表现赶不上对应的有限网络。通过比较核方法与有限宽度架构(使用小学习率,无正则化)的 base 模型,并逐一验证可打破(大学习率、L2 正则化)或改进(集成)无限宽度与核方法对应性的训练实践的效果,研究者验证了这一假设。结果见下图 1:

    图 1:有限和无限网络及其变体在 CIFAR-10 上的测试准确率。从给定架构类别的有限宽度 base 网络开始,标准和 NTK 参数化的模型表现随着修改而发生变化:+C 指居中(Centering)、+LR 指大学习率、+U 指通过早停实现欠拟合、+ZCA 指使用 ZCA 正则化进行输入预处理、+Ens 指多个初始化集成,另外还有一些组合方案。Lin 指线性化 base 网络的性能。

    从中可以观察到,对于 base 有限网络,无限 FCN 和 CNN-VEC 的表现要优于它们各自对应的有限网络。另一方面,无限 CNN-GAP 网络的表现又比其对应的有限版本差。研究者指出这其实与架构有关。举例来说,即使有限宽度 FCN 网络组合了高学习率、L2 和欠拟合等多种不同技巧,无限 FCN 网络的性能还是更优。只有再加上集成之后,有限网络的性能才能达到相近程度。

    另一个有趣的观察是,ZCA 正则化预处理能显著提升 CNN-GAP 核的表现

    2. NNGP 通常优于 NTK

    从下图 2 中可以看出,在 CIFAR-10、CIFAR-100 和 Fashion-MNIST 数据集上 NNGP 的性能持续优于 NTK。NNGP 核不仅能得到更强的模型,而且所需的内存和计算量也仅有对应的 NTK 的一半左右,而且某些性能最高的核根本就没有对应的 NTK 版本。

    图 2:当对角正则化经过精心调整时,NNGP 在图像分类任务上通常优于 NTK。

    3. 居中和集成有限网络都会得到类 kernel 的表现

    图 3:居中可以加速训练和提升性能。

    图 4:集成 base 网络可让它们达到与核方法相媲美的表现,并且在非线性 CNN 上还优于核方法。

    4. 大学习率和 L2 正则化会让有限网络和核之间出现差异

    从上图 1 中可以观察到,大学习率(LR)的效果容易受到架构和参数化的影响。

    L2 正则化则能稳定地提升所有架构和参数化的性能(+1-2%)。即使使用经过精心调节的 L2 正则化,有限宽度 CNN-VEC 和 FCN 依然比不上 NNGP/NTK。L2 结合早停能为有限宽度 CNN-VEC 带来 10-15% 的显著性能提升,使其超过 NNGP/NTK。

    5. 使用标准参数化能为网络提升 L2 正则化

    图 5:受 NTK 启发的逐层扩展能让 L2 正则化在标准参数化网络中更有帮助。

    研究者发现,相比于使用标准参数化,使用 NTK 参数化时 L2 正则化能为有限宽度网络带来显著的性能提升。使用两种参数化的网络的权重之间存在双射映射。受 NTK 参数化中 L2 正则化项性能提升的启发,研究者使用这一映射构建了一个可用于标准参数化网络的正则化项,其得到的惩罚项与原版 L2 正则化在对应的 NTK 参数化网络上得到的一样。

    6. 在超过两次下降的宽度中,性能表现可能是非单调的

    图 6:有限宽度网络在宽度增大时通常会有更好的表现,但 CNN-VEC 表现出了出人意料的非单调行为。L2:在训练阶段允许非零权重衰减,LR:允许大学习率,虚线表示允许欠拟合(U)。

    7. 核对角正则化的行为类似于早停

    图 7:对角核正则化的行为类似于早停。实线对应具备不同对角正则化 ε 的 NTK 推断;虚线对应梯度下降到时间 τ = ηt 后的预测结果,线条颜色表示不同的训练集大小 m。在时间 t 执行早停紧密对应于使用系数 ε = Km/ηt 的正则化,其中 K=10 表示输出类别的数量。

    8. 浮点数精度决定了核方法失败的关键数据集大小

    图 8:无限网络核的尾部特征值表现出了幂律衰减趋势。

    9. 由于条件不好,线性化 CNN-GAP 模型表现很差

    研究者观察到线性化 CNN-GAP 在训练集上的收敛速度非常慢,导致其验证表现也很差(见上图 3)。

    这一结果的原因是池化网络的条件很差。Xiao 等人的研究 [33] 表明 CNN-GAP 网络初始化的条件比 FCN 或 CNN-VEC 网络差了像素数倍(对 CIFAR-10 来说是 1024)。

    表 1:对应架构类型的核的 CIFAR-10 测试准确率。

    10. 正则化 ZCA 白化(whitening)可提升准确率

    图 9:正则化 ZCA 白化可提升有限和无限宽度网络的图像分类性能。所有的图都将性能表现为 ZCA 正则化强度的函数。a)在 CIFAR-10、Fashion-MNIST、CIFAR-100 上核方法输入的 ZCA 白化;b)有限宽度网络输入的 ZCA 白化。

    11. 同变性(equivariance)仅对远离核区域的窄网络有益

    图 10:同变性仅在核区域之外的 CNN 模型中得到利用。

    如果 CNN 模型能有效地利用同变性,则预计它能比 FCN 更稳健地处理裁剪和平移。出人意料的是,宽 CNN-VEC 的性能会随输入扰动的幅度而下降,而且下降速度与 FCN 一样快,这说明同变性并未得到利用。相反,使用权重衰减的窄模型(CNN-VEC+L2+narrow)的性能下降速度要慢得多。正如预期,平移不变型 CNN-GAP 依然是最稳健的。

    12. 集成核预测器可使用 NNGP/NTK 进行实用的数据增强

    图 11:集成核预测器(ensembling kernel predictors)可使基于大规模增强数据集的预测在计算上可行。

    可以观察到,DA 集成可提升准确率,且相比于 NTK,它对 NNGP 的效果要好得多。

    这里研究者提出了一种直接让集成核预测器实现更广泛的数据增强的方法。该策略涉及到构建一组经过增强的数据批,为其中每一批执行核推断,然后执行所得结果的集成。这相当于用模块对角近似替代核,其中每个模块都对应一个数据批,所有增强的数据批的并集即为完整的增强数据集。该方法在该研究所有无线宽度架构的对应核方法上都取得了当前最佳结果。 

    编辑 ∑Gemini

    来源:arXiv

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  • 网络安全论文

    2012-04-12 16:48:05
    这是一篇有关网络安全方面的论文,希望可以帮助到大家。
  • 与基于深度学习/深度神经网络的图像压缩和视频编码框架有关的最新论文和代码。 2016年 [Google] George Toderici,Sean M. O'Malley,Sung Jin Hwang,Damien Vincent,David Minnen,Shumeet Baluja,Michele ...
  • 有关病毒在复杂网络中传播与控制方面20篇论文 PaperDOI,发表期刊(会议)以及中英文摘要都已写出,大家可根据doi在scihub进行下载。仅仅只是为了记录一下。 1.Optimal ptimal control of an SIVRS epidemic ...

    有关病毒在复杂网络中传播与控制方面的20篇论文

    Paper的DOI,发表期刊(会议)以及中英文摘要都已写出,大家可根据doi在scihub进行下载。仅仅只是为了记录一下。

    1.Optimal ptimal control of an SIVRS epidemic spreading model with virus variation based on complex networks
    Author links open overlay panelXuDegangXuXiyangXieYongfangYangChunhua
    https://doi.org/10.1016/j.cnsns.2016.12.025
    ScienceDirect —Communications in Nonlinear Science and Numerical Simulation
    被引用次数:30

    摘要
    本文提出并研究了一种新的传染病传播的SIVRS数学模型。该模型考虑了基于复杂网络的传染病传播过程中的病毒变异因素,这个复杂网络能够描述网络中易感者、感染者、变异者和恢复者等不同致病因子的不同接触状态。我制定了一个最佳控制问题,以有限的资源分配来最大化回收的病原体,并研究了易感,感染和变异的最佳控制策略。然后,基于庞特里安最小原理和改进的前向后扫技术,给出了最优控制问题的解的可能性。并通过数值模拟,说明了理论结果。

    Abstract
    A novel SIVRS mathematical model for infectious diseases spreading is proposed and investigated in this paper. In this model virus variation factors are considered in the process of epidemic spreading based on complex networks, which can describe different contact status for different agents including the susceptible, the infectious, the variant and the recovered in a network. An optimal control problem is formulated to maximize the recovered agents with the limited resource allocation and optimal control strategies over the susceptible, the infected and the variant are investigated. Then the existence of a solution to the optimal control problem is given based on Pontryagin’s Minimum Principle and modified forward backward sweep technique. Numerical simulations are provided to illustrate obtained theoretical results.

    2.Research on computer virus source modeling with immune characteristics
    Published in: 2017 29th Chinese Control And Decision Conference (CCDC)
    Date of Conference: 28-30 May 2017
    Date Added to IEEE Xplore: 17 July 2017
    ISBN Information:
    Electronic ISSN: 1948-9447
    INSPEC Accession Number: 17041134
    DOI: 10.1109/CCDC.2017.7979312
    被引用次数:2

    摘要:
    现有的计算机病毒研究方向主要集中在病毒传播的特征上,很少有人研究计算机病毒的来源问题,但是计算机病毒的来源可以消除病毒的来源,从而迅速抑制计算机病毒的传播。因此,本文主要对病毒源问题进行建模,通过观察网络中各个节点的状态来实现对病毒源的定位。首先,根据实际网络连接情况,建立了基于复杂网络的计算机病毒源模型。其次,利用网络时序状态矩阵模拟计算机病毒传播引起的网络节点状态。并考虑到计算机节点的免疫特性。通过分析,得到了网络状态转换过程,病毒节点的初始状态为计算机病毒源。最后,本文给出了仿真实验,实验证明了上述模型的正确性和有效性,为今后有效控制计算机病毒传播提供了帮助。

    Abstract:
    Existing computer virus direction of the study focused on the characteristics of virus propagation, and few people study the problem of computer virus source, but the source of computer virus can eliminate the source of the virus to quickly inhibit the spread of computer viruses. Therefore, the article mainly on the source of the virus problem modeling, by observing the status of each node network to achieve the source of the virus location. First, according to the actual network connection, to build a computer virus source model based on complex network. Secondly, the network timing state matrix is used to simulate the state of network nodes caused by computer virus propagation. And taking into account the computer node immune characteristics. Through analysis, the network state transition process is obtained, and the initial state of the virus node is the computer virus source. Finally, the paper gives the simulation experiment, the experiment proved that the above model is correct and effective, for the future effective control of computer virus propagation to help.

    3.Global attractivity and optimal dynamic countermeasure of a virus propagation model in complex networks
    Author links open overlay panelXulongZhangChenquanGan
    https://doi.org/10.1016/j.physa.2017.08.085
    ScienceDirect —Physica A: Statistical Mechanics and its Applications
    被引用次数:13

    摘要
    本文旨在研究对策和网络拓扑对病毒传播和最佳动态对策的综合影响。提出并分析了一种一种新型的异质传播模型及其最优控制问题。定性分析表明,所提出模型的唯一均衡具有全局吸引力,并且最优控制问题具有最优控制。我们还进行了一些模拟实验。具体而言,发现我们获得的结果与先前的一些结果相反,并且将对策散布到较高等级的节点比散布到较低等级的节点更有效。我们对此做出了相关解释。实验表明网络拓扑结构和对抗措施在抑制病毒传播中起着重要作用。

    Abstract
    This paper aims to study the combined impact of countermeasure and network topology on virus diffusion and optimal dynamic countermeasure. A novel heterogenous propagation model and its optimal control problem are proposed and analyzed. Qualitative analysis shows that the unique equilibrium of the proposed model is globally attractive and the optimal control problem has an optimal control. Some simulation experiments are also performed. Specifically, it is found that our obtained results are contrary to some previous results and countermeasure dissemination to higher-degree nodes is more effective than that to lower-degree nodes. The related explanations are also made. This indicates that countermeasures and network topology play an important role in suppressing viral spread.

    1. A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks
      Published in: IEEE Transactions on Control of Network Systems ( Volume: 7, Issue: 2, June 2020)
      Page(s): 944 - 955
      Date of Publication: 29 July 2019
      ISSN Information:
      INSPEC Accession Number: 19762783
      DOI: 10.1109/TCNS.2019.2931862
      Publisher: IEEE
      被引用次数:12

    摘要:
    通信网络连接性的提高使得大规模的网络分布式处理成为可能,并提高了信息交换的效率。然而,恶意软件和病毒可以利用高连通性在网络上传播,并控制设备和服务器用于非法目的。本文利用易感传染病模型来捕捉病毒在网络上的传播过程,并提出一种抗病毒的权值自适应策略来缓解病毒在网络上的传播。我们提出了一个差分博弈框架,为分散缓解网络中的节点不能完全协调,每个节点根据与相邻节点的局部交互来确定自己的控制策略提供了理论基础。我们刻画和检验了纳什均衡的结构,并从最小化整个网络的总成本的角度讨论了纳什均衡的无效性。我们提出了一种通过惩罚机制来降低纳什均衡效率的机制设计,使得分散的策略能够实现整个网络的社会福利。我们用数值实验证实了我们的结果,并表明分布权重自适应方案可以实现病毒抗性。

    Abstract:
    Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency of information exchange. However, malware and a virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use a susceptible-infected-susceptible epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus resistance can be achieved by a distributed weight adaptation scheme.

    1. Dynamic stability of an SIQS epidemic network and its optimal control
      Author links open overlay panelKezanLiabGuanghuZhuaZhongjunMaaLijuanChenc
      https://doi.org/10.1016/j.cnsns.2018.06.020
      ScienceDirect —Communications in Nonlinear Science and Numerical Simulation
      被引用次数:21

    摘要
    为了更好地理解和利用传染病暴发时的检疫控制,本文引入了一个复杂网络上的非线性SIQS传染病模型。我们利用复网络理论和Lyapunov函数方法,得到了无病平衡点和地方病平衡点的基本再生数和全局稳定性。此外,为了降低控制成本,我们研究了最优检疫控制问题。我们应用最优控制理论,得到了最优控制和模型最优解的存在唯一性。数值算例验证了这些结果,我们也研究了网络结构对最优控制的影响。

    Abstract
    In order to better understand and utilize the quarantine control when encountering outbreaks of infectious diseases, this paper introduces a nonlinear SIQS epidemic model on complex networks. By using complex network theory and Lyapunov function method, we obtain its basic reproduction number and global stability of both disease-free equilibrium and endemic equilibrium. Moreover, we investigate the optimal quarantine control problem for reducing control cost. By applying the optimal control theory, we obtain existence and uniqueness of the optimal control and the model’s optimal solution. These results are verified by some numerical examples, and the influence of network structure on the optimal control is also studied.

    1. A new computer virus model with impulsive vaccination and saturation effect
      Published in: 2017 International Workshop on Complex Systems and Networks (IWCSN)
      Date of Conference: 8-10 Dec. 2017
      Date Added to IEEE Xplore: 01 February 2018
      ISBN Information:
      INSPEC Accession Number: 17558744
      DOI: 10.1109/IWCSN.2017.8276524

    摘要:
    本文研究了一类具有脉冲接种和饱和效应的新数学模型的定性问题。我们的目的是了解脉冲接种对电子病毒传播的影响。因此我们建立了一个新的数学模型。我们对该模型的全面分析,说明模型的动力学性质与基本再生比有关。数值算例也证明了该模型的正确性。

    Abstract:
    This paper investigates the qualitative issue of a new mathematical model with impulsive vaccination and saturation effect. It aims to understand how the impulsive vaccination affects the spread of electronic viruses. So we establish a new mathematical model. A complete analysis of this model shows that the dynamical properties of the model is relating to the basic reproduction ratio. Some numerical examples also justify the proposed model.

    7.Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks
    Published in: IEEE Transactions on Cybernetics ( Early Access )
    Page(s): 1 - 15
    Date of Publication: 13 March 2020
    ISSN Information:
    PubMed ID: 32175884
    DOI: 10.1109/TCYB.2020.2975530
    Publisher: IEEE

    摘要:
    在预算有限的情况下控制病毒在复杂网络中的传播已经引起了广泛的关注,但仍然具有挑战性。本文旨在解决病毒传播控制中的组合离散资源分配问题。为了应对日益增长的网络规模和提高求解效率的挑战,我们提出了一种进化分治算法,即基于网络社区分解的协同进化算法(NCD-CEA)。它的特点是以社区为基础的划分技术和协同进化征服思想。首先,为了降低时间复杂度,NCD-CEA采用改进的社团检测方法将网络划分为多个社团,使得解空间中最相关的变量聚集在一起。然后将问题和全局群分解为具有低维嵌入的子问题和子群。其次,为了获得高质量的解,设计了一种替代的进化方法,依次促进子群和全局群的进化,子解由局部适应度函数估计,全局解由全局适应度函数估计。在不同网络上的大量实验表明,NCD-CEA在解决RAPs问题上具有很强的竞争力。本文让控制病毒在大规模网络上传播迈进了一步。

    Abstract:
    The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA). It is characterized by the community-based dividing technique and cooperative coevolution conquering thought. First, to reduce the time complexity, NCD-CEA divides a network into multiple communities by a modified community detection method such that the most relevant variables in the solution space are clustered together. The problem and the global swarm are subsequently decomposed into subproblems and subswarms with low-dimensional embeddings. Second, to obtain high-quality solutions, an alternative evolutionary approach is designed by promoting the evolution of subswarms and the global swarm, in turn, with subsolutions evaluated by local fitness functions and global solutions evaluated by a global fitness function. Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.

    1. On the Optimal Dynamic Control Strategy of Disruptive Computer Virus
      Jichao Bi ,1 Xiaofan Yang ,1 Yingbo Wu ,1 Qingyu Xiong ,1 Junhao Wen,1 and Yuan Yan Tang
      Academic Editor: Yong Deng
      Research Article | Open Access
      Epidemic Processes on Complex Networks
      Volume 2017 |Article ID 8390784 | https://doi.org/10.1155/2017/8390784
      被引用次数:19

    摘要
    破坏性的计算机病毒造成了巨大的经济损失。本文提出了一种具有成本效益的破坏性病毒动态控制策略。首先,将开发问题建模为最优控制问题。其次,给出了最优控制存在性的判据。第三,导出了最优性系统。接下来,本文给出了最优动态控制策略的一些例子。最后,对实际动态控制策略的性能进行了评价。

    Abstract
    Disruptive computer viruses have inflicted huge economic losses. This paper addresses the development of a cost-effective dynamic control strategy of disruptive viruses. First, the development problem is modeled as an optimal control problem. Second, a criterion for the existence of an optimal control is given. Third, the optimality system is derived. Next, some examples of the optimal dynamic control strategy are presented. Finally, the performance of actual dynamic control strategies is evaluated.

    1. A theoretical method to evaluate honeynet potency
      Author links open overlay panelJianguoRenabChunmingZhangcQihongHaod
      https://doi.org/10.1016/j.future.2020.08.021
      Future Generation Computer Systems
      Volume 116, March 2021, Pages 76-85

    摘要
    蜜网是一种易受攻击的模拟计算机网络,通常用于提高网络安全性。对蜜网效力的深入评估对于蜜网的有效设计和改进至关重要。为此,本文提出并分析了一种新的评价蜜网效价的动力学模型。该模型将蜜罐作为状态变量引入到模型中,我们通过数学分析发现,蜜罐的最大特征值是决定蜜罐效能的关键。特别是,特征值的范围明确地形成了两个明确分支之间感染传播的界限,在这一界限之下,蜜网以最佳水平工作,直到恶意软件趋于灭绝,而在这一界限之上,恶意软件以一定的水平持续存在。在几种典型的计算机网络上进行了数值模拟,验证了该模型的正确性。在理论和数值计算的基础上进行了讨论。结果表明,通过适当减少链路数和最大节点度,或者加强蜜罐的数据控制或补丁反馈功能,可以明显提高蜜罐的效能。研究结果可为有效的蜜网设计提供指导。

    Abstract
    The honeynet is a vulnerable and simulated computer network that is commonly used to improve network security. A profound evaluation of honeynet potency is crucial to the effective design and improvement of a honeynet. For that purpose, a new dynamical model for evaluating the honeynet potency is proposed and analyzed in this paper. The proposed model incorporates honeypots into the model formulation as state variables, and mathematical analysis finds that a key role for deciding the honeynet potency is the greatest characteristic value of the deployed network. Particularly, the range of characteristic values clearly forms the delimitation among the infection propagation between two explicit embranchments, below which the honeynet works at its best level, until malware tends toward the extinction, and above which the malware persists at a certain level. The proposed model is verified by numerical simulations on several representative computer networks. Based on the theoretical and numerical results, a discussion is provided. Accordingly, the results show that the honeynet potency can be explicitly enhanced by either properly reducing the number of links and the greatest node degree of the deployed network, or strengthening the data control or patch feedback function of the honeypot. The results presented in this paper can provide guidance on effective honeynet design.

    1. A survey on modelling of infectious disease spread and control on social contact networks
      Comments: Under submission. part of arXiv:1906.02856
      Subjects: Physics and Society (physics.soc-ph); Social and Information Networks (cs.SI); Populations and Evolution (q-bio.PE)
      ACM classes: F.2.2

    摘要
    尽管根据世界卫生组织(WHO)的报告,每年约有420万人死于传染病,但在COVID-19发病之前,人类社会就已经被过度视为传染病的严重威胁。由于最近的COVID-19大流行,2020年期间有200多万人死亡,9620万人受到这种毁灭性疾病的影响。最近的研究表明,通过对传染病在社会接触网络上的传播进行建模,应用个人互动和移动数据有助于管理大流行。传染病的传播可以用扩散过程的理论和方法来解释,在这个过程中,一个动态现象在网络系统上演化。在扩散过程的建模中,假设传染项通过节点间的相互作用在网络系统中扩散。这类似于传染性病毒的传播,例如COVID-19的传播,通过个体社会互动在人群中传播。扩散过程的演化行为受系统特征和扩散过程本身机制的强烈影响。因此,传染病的传播可以通过人与人之间的相互作用以及疾病本身的特点来解释。本文介绍了传染病扩散过程的相关理论和方法。

    Abstract
    Infectious diseases are a significant threat to human society which was over sighted before the incidence of COVID-19, although according to the report of the World Health Organisation (WHO) about 4.2 million people die annually due to infectious disease. Due to recent COVID-19 pandemic, more than 2 million people died during 2020 and 96.2 million people got affected by this devastating disease. Recent research shows that applying individual interactions and movements data could help managing the pandemic though modelling the spread of infectious diseases on social contact networks. Infectious disease spreading can be explained with the theories and methods of diffusion processes where a dynamic phenomena evolves on networked systems. In the modelling of diffusion process, it is assumed that contagious items spread out in the networked system through the inter-node interactions. This resembles spreading of infectious virus, e.g. spread of COVID-19, within a population through individual social interactions. The evolution behaviours of the diffusion process are strongly influenced by the characteristics of the underlying system and the mechanism of the diffusion process itself. Thus, spreading of infectious disease can be explained how people interact with each other and by the characteristics of the disease itself. This paper presenters the relevant theories and methodologies of diffusion process that can be used to model the spread of infectious diseases.

    1. Global dynamics of a network-based WSIS model for mobile malware propagation over complex networks
      Author links open overlay panelShouyingHuang
      https://doi.org/10.1016/j.physa.2018.02.117
      Physica A: Statistical Mechanics and its Applications
      Volume 503, 1 August 2018, Pages 293-303

    摘要
    为了理解用户安全意识对恶意软件在移动网络上长期传播行为的影响,本文深入研究了一种新的基于网络的弱保护和强保护敏感节点传染病模型的全局动力学。分析和数值结果都表明,模型的全局动力学完全受一个阈值控制。具体地,我们证明了当值小于1时,无恶意均衡是全局渐近稳定的,移动恶意软件将消失。当该值大于1时,移动恶意软件将在网络上持续存在,同时存在唯一的恶意软件均衡,在一定条件下全局渐近稳定。所得结果改进和丰富了一些已知的结果。有趣的是,提高受感染节点的恢复率会导致受强保护敏感节点的增加和阈值的降低。该研究对有效控制移动恶意软件的传播具有重要的指导意义。

    Abstract
    For understanding the influence of user security awareness on the long-term spreading behavior of malware over mobile networks, in this paper, we intensively study the global dynamics of a novel network-based epidemic model with weakly-protected and strongly-protected susceptible nodes. Both analytical and numerical results show that the global dynamics of the model is completely governed by a threshold value. Specifically, we prove that when the value is lower than one, the malware-free equilibrium is globally asymptotically stable and mobile malware will disappear. When the value is greater than one, mobile malware will persist on the network, and in the meantime there exists a unique malware equilibrium which is globally asymptotically stable under certain conditions. The obtained results improve and enrich some known ones. Interestingly, increasing the recovery rate of infected nodes can result in the increase of strongly-protected susceptible nodes and the decrease of the threshold value. The study has valuable guiding significance in effectively controlling mobile malware spread.

    1. The impact of patch forwarding on the prevalence of computer virus: A theoretical assessment approach
      Author links open overlay panelLu-XingYangabXiaofanYangaYingboWua
      https://doi.org/10.1016/j.apm.2016.10.028
      Under an Elsevier user license
      Applied Mathematical Modelling
      Volume 43, March 2017, Pages 110-125

    摘要
    病毒补丁可以通过计算机网络迅速传播,并在安装后立即生效,这大大增强了它们的含病毒能力。本文旨在从理论上评估补丁转发对计算机病毒流行的影响。为此,我们提出了一种新的恶意软件流行模型,该模型充分考虑了补丁转发的影响。本文揭示了模型的动力学过程。特别地,除了永久的易感平衡外,这个模型还可以接纳一个感染的或修补的或混合的平衡。给出了四个平衡点的全局稳定性判据,并给出了数值算例。结果表明,补丁转发网络和病毒传播网络的频谱半径对计算机病毒的流行有显著影响。本文还揭示了一些关键因素对病毒流行的影响。基于这些发现,本文推荐了一些包含电子病毒的策略。

    Abstract
    Virus patches can be disseminated rapidly through computer networks and take effect as soon as they have been installed, which significantly enhances their virus-containing capability. This paper aims to theoretically assess the impact of patch forwarding on the prevalence of computer virus. For that purpose, a new malware epidemic model, which takes into full account the influence of patch forwarding, is proposed. The dynamics of the model is revealed. Specifically, besides the permanent susceptible equilibrium, this model may admit an infected or a patched or a mixed equilibrium. Criteria for the global stability of the four equilibria are given, respectively, accompanied with numerical examples. The obtained results show that the spectral radii of the patch-forwarding network and the virus-spreading network both have a marked impact on the prevalence of computer virus. The influence of some key factors on the prevalence of virus is also revealed. Based on these findings, some strategies of containing electronic virus are recommended.

    1. Modeling the spread of computer virus via Caputo fractional derivative and the beta-derivative
      Ebenezer Bonyah, Abdon Atangana & Muhammad Altaf Khan
      https://doi.org/10.1186/s40540-016-0019-1

    摘要
    随着科学技术成为一切经济的驱动力,信息科学的概念在人类发展中是必然的。流行病期间一个人之间的联系至关重要,这可以用数学原理来研究。在本项研究中,Piqueira等人(计算机科学杂志1:31−34,2005)和Piqueira和Araujo(应用数学计算机2(213):355−360,2009)通过Caputo和beta衍生物研究了一个公认的计算机病毒模型。本文对扩展模型的稳定性分析进行了较详细的讨论。利用拉普拉斯摄动法和同伦分解技术求解了扩展模型的解析解。本文对扩展模型的每种迭代方法进行了序贯总结。利用Piqueira和Araujo(Appl Math Comput 2(213):355−3602009)中的参数,本文给出了一些数值模拟结果。

    Abstract
    The concept of information science is inevitable in the human development as science and technology has become the driving force of all economics. The connection of one human being during epidemics is vital and can be studied using mathematical principles. In this study, a well-recognized model of computer virus by Piqueira et al. (J Comput Sci 1:31−34, 2005) and Piqueira and Araujo (Appl Math Comput 2(213):355−360, 2009) is investigated through the Caputo and beta-derivatives. A less detail of stability analysis was discussed on the extended model. The analytical solution of the extended model was solved via the Laplace perturbation method and the homotopy decomposition technique. The sequential summary of each of iteration method for the extend model was presented. Using the parameters in Piqueira and Araujo (Appl Math Comput 2(213):355−360, 2009), some numerical simulation results are presented.

    1. ILSR rumor spreading model with degree in complex network
      Author links open overlay panelAnzhiYangXianyingHuangXiumeiCaiXiaofeiZhuLingLu
      https://doi.org/10.1016/j.physa.2019.121807
      Physica A: Statistical Mechanics and its Applications
      Volume 531, 1 October 2019, 121807
      被引用次数:11

    摘要
    社交网络中的大多数谣言危害极大,对社会福利产生重大负面影响。因此,探讨谣言传播规律已成为当前研究的热点之一。传统的谣言传播模型大多基于传染病传播模型,如SIR。由于没有考虑个体差异和网络结构对谣言传播的影响,复杂网络中的谣言传播过程只能用粗粒度的方式来描述。本文研究了不同用户在谣言传播中的作用。根据网络中不同节点的程度,为每个节点设计了一个新的状态转移函数,并提出了一个新的谣言传播ILSR模型。首先对模型进行了实验,计算了平衡点和基本再生数,证明了模型的合理性。然后在WS网络、BA无标度网络和一个真实的Facebook网络上进行了实验,研究了不同节点之间的时间关系以及网络结构对谣言传播的影响,实验结果表明了模型的正确性和有效性。本文为探索谣言在复杂网络中的传播规律,指导和控制谣言的传播提供参考。

    Abstract
    Most rumors in social networks are extremely harmful and have a significant negative impact on social welfare. Therefore, exploring the laws of rumor propagation has been one of the hot topics in current researches. Most traditional rumor spreading models are based on infectious disease transmission models, such as SIR. Since the influence of individual differences and the network structure on rumor spreading are not considered, the rumor propagation process in complex networks can only be described in a coarse-grained manner. In this paper, we consider the role of different users in rumor propagation. Based on the degree of different nodes in the network, we design a new state transition function for each node and proposed a new rumor propagation ILSR model. Firstly, we analyze the model, calculate the equilibrium point and the basic reproductive number to prove the rationality of the model. Then experiments are performed in WS networks, BA scale-free networks and a real Facebook network to investigate the relationship between various nodes with time and the impact of network structure on rumor propagation, and the experimental results show the correctness and effectiveness of the model. It provides a reference for exploring the propagation law of rumors in complex networks and guiding and controlling the propagation of rumors.

    15.The impact of information spreading on epidemic vaccination game dynamics in a heterogeneous complex network- A theoretical approach
    Author links open overlay panelKM ArifulKabirabKazukiKugaaJunTanimotoca
    https://doi.org/10.1016/j.chaos.2019.109548
    Chaos, Solitons & Fractals
    Volume 132, March 2020, 109548
    被引用次数:19

    摘要
    本文提出了一种改进的免疫恢复型(SIR/V)免疫模型,并利用非感知(UA)模型,研究了信息在免疫博弈空间结构中传播对疫情动态的影响。两层SIR/V流行病模型被认为是为了阐明信息传播,在这种情况下,敏感、接种和感染的个体的比例被分割为不知情和意识状态,因为每个易感者和接种疫苗的人都与受感染的邻居通过空间结构(例如,一个基础网络)相联系。文中推导了一个季节内具有意识影响动态的流行病疫苗博弈,然后是一个策略更新过程,该过程指的是个体是否接种不完善疫苗。我们考虑了两种不同的策略更新规则:基于个体的风险评估(IB-RA)和基于策略的风险评估(SB-RA),以探讨不同的基础网络拓扑,如随机图和无标度网络,然后通过信息传播对流行病的影响。因此,在无标度网络中,除了随机图和均匀网络之外,意识可以提高疫情阈值的有效性,减少感染的蔓延。

    Abstract
    A modified susceptible-vaccinated-infected-recovered (SIR/V) with unaware-aware (UA) epidemic model in heterogeneous networks is presented to study the effect of information spreading in the spatial structure of the vaccination game on epidemic dynamics. Two layers SIR/V epidemic model is considered to elucidate information spreading, where the fraction of susceptible, vaccinated and infected individuals are parted as unaware and aware state as each susceptible and vaccinated persons are allied with their infected neighbors by a spatial structure, say, an underlying network. The context deduces epidemic vaccination game with awareness influence dynamics in one single season followed by a strategy update process that refer an individual to take imperfect vaccination or not. We considered two different strategy updating rules: individual based risk assessment (IB-RA) and strategy-based risk assessment (SB-RA) to explore how different underlying network topologies, say, random graph and scale free networks, subsequently giving impact on the final epidemic size, vaccination coverage and average social payoff through the effect of information spreading on epidemic. Thus, it can be seen that, awareness can enhance the epidemic threshold effectiveness and lessen the spreading of infection in a scale free network other than random graph and homogeneous network.

    1. Disease spreading in complex networks: A numerical study with Principal Component Analysis
      Author links open overlay panelP.H.T.SchimitaF.H.Pereiraab
      https://doi.org/10.1016/j.eswa.2017.12.021
      Expert Systems with Applications
      Volume 97, 1 May 2018, Pages 41-50
      被引用次数:16

    摘要
    疾病传播模型需要一个群体模型来组织个体在空间中的分布以及它们之间的联系。通常情况下,病原体(细菌、病毒)通过这些连接在个体之间传播,从而发生流行病爆发。在这里,将使用复杂的网络模型为人口建模,如Erdös–Rényi、Small World、Scale Free和Barábasi–Albert,因为它们被用于社会网络;疾病将由SIR(易感-感染-恢复)模型建模。这项工作的目的是,不管网络/人群模型如何,分析哪些拓扑参数与疾病的成败更相关。因此,本文对SIR模型进行了大范围的仿真,并进行了初步分析。利用所有的模拟数据,进行主成分分析(PCA)的研究,以找出最相关的拓扑和疾病参数。

    Abstract
    Disease spreading models need a population model to organize how individuals are distributed over space and how they are connected. Usually, disease agent (bacteria, virus) passes between individuals through these connections and an epidemic outbreak may occur. Here, complex networks models, like Erdös–Rényi, Small-World, Scale-Free and Barábasi–Albert will be used for modeling a population, since they are used for social networks; and the disease will be modeled by a SIR (Susceptible–Infected–Recovered) model. The objective of this work is, regardless of the network/population model, analyze which topological parameters are more relevant for a disease success or failure. Therefore, the SIR model is simulated in a wide range of each network model and a first analysis is done. By using data from all simulations, an investigation with Principal Component Analysis (PCA) is done in order to find the most relevant topological and disease parameters.

    1. Analysis and Control of a Continuous-Time Bi-Virus Model
      Publisher: IEEE
      Published in: IEEE Transactions on Automatic Control ( Volume: 64, Issue: 12, Dec. 2019)
      Page(s): 4891 - 4906
      Date of Publication: 11 February 2019
      ISSN Information:
      INSPEC Accession Number: 19194702
      DOI: 10.1109/TAC.2019.2898515
      被引用次数:28

    摘要:
    本文研究了一种分布式连续时间双病毒模型,其中两种竞争病毒在由多个个体组成的网络上传播。通过分析系统的平衡和系统的稳定性,对网络的极限行为进行了表征。具体而言,当两种病毒传播到可能不同的定向感染图上时,系统可能会有以下情况:一是一个独特的平衡,即全球稳定的健康状态,意味着最终两种病毒都将被消灭;第二,两种平衡包括健康状态和显性病毒状态,这几乎是全球稳定的,这意味着一种病毒将扩散到整个网络,造成一种病毒流行病,而另一种病毒将被根除,或者说,根据愈合和感染率的特定条件,至少有三种平衡,包括健康状态和两种主要病毒状态。当两种病毒在同一个有向感染图上传播时,系统可能存在零个或无限多个共存的流行病平衡,这代表了两种病毒的扩散。在分散控制技术的背景下,我们研究了一些非平凡平衡的灵敏度性质,并给出了一种不可能的结果。

    Abstract:
    This paper studies a distributed continuous-time bi-virus model in which two competing viruses spread over a network consisting of multiple groups of individuals. Limiting behaviors of the network are characterized by analyzing the equilibria of the system and their stability. Specifically, when the two viruses spread over possibly different directed infection graphs, the system may have the following: first, a unique equilibrium, the healthy state, which is globally stable, implying that both viruses will eventually be eradicated, second, two equilibria including the healthy state and a dominant virus state, which is almost globally stable, implying that one virus will pervade the entire network causing a single-virus epidemic while the other virus will be eradicated, or third, at least three equilibria including the healthy state and two dominant virus states, depending on certain conditions on the healing and infection rates. When the two viruses spread over the same directed infection graph, the system may have zero or infinitely many coexisting epidemic equilibria, which represents the pervasion of the two viruses. Sensitivity properties of some nontrivial equilibria are investigated in the context of a decentralized control technique, and an impossibility result is given for a certain type of distributed feedback controller.

    1. A compartmental model for computer virus propagation with kill signals
      https://doi.org/10.1016/j.physa.2017.05.038
      Physica A: Statistical Mechanics and its Applications
      Volume 486, 15 November 2017, Pages 446-454

    摘要
    研究计算机病毒的杀毒信号对计算机用户具有重要意义。杀戮信号允许计算机用户事先采取预防措施。本文提出了一种基于杀灭信号的计算机病毒传播模型SEIR-KS模型,并对该模型的动态特性进行了理论分析。本文得到了一个传染阈值,并研究了病毒平衡点的存在唯一性。我们应用Routh-Hurwitz准则和Lyapunov泛函方法证明了无病毒平衡点和病毒平衡点是局部和全局渐近稳定的。数值模拟结果验证了理论分析的正确性。该模型的有效性已通过以下观察得到验证:(1)与相关文献中的模型相比,该模型中感染节点的密度下降到75%左右;(2)较高的KS密度有利于抑制病毒的扩散。

    Abstract
    Research in the area of kill signals for prevention of computer virus is of significant importance for computer users. The kill signals allow computer users to take precautions beforehand. In this paper, a computer virus propagation model based on the kill signals, called SEIR-KS model, is formulated and full dynamics of the proposed model are theoretically analyzed. An epidemic threshold is obtained and the existence and uniqueness of the virus equilibrium are investigated. It is proved that the virus-free equilibrium and virus equilibrium are locally and globally asymptotically stable by applying Routh–Hurwitz criterion and Lyapunov functional approach. The results of numerical simulations are provided that verifies the theoretical results. The availability of the proposed model has been validated with following observations: (1) the density of infected nodes in the proposed model drops to approximately 75% compared to the model in related literature; and (2) a higher density of KS is conductive to inhibition of virus diffusion.

    1. Modeling epidemic spread in transportation networks: A review
      Author links open overlay panelJianLiabcTaoXiangabLinghuiHeac
      https://doi.org/10.1016/j.jtte.2020.10.003
      Journal of Traffic and Transportation Engineering (English Edition)
      Available online 5 January 2021

    摘要
    新型传染病的出现已成为一个严重的全球性问题。便利的交通网络导致全球化背景下的快速动员,这是传染病迅速传播的一个重要因素。运输系统可以在传染病流行期间造成病毒传播,但它们也支持在传染病流行后重新开放经济。因此,了解流动性对传染病传播的影响机制以及建立传染病在交通网络中传播的风险模型是非常重要的。本文综述了各种传染病传播模型的基本结构和应用,包括数学模型、统计模型、网络模型和仿真模型。分析了模型在交通系统中应用的优点和局限性,包括传染病传播的动态特性和管理控制的决策支持。最后,本文讨论了研究趋势和展望。建议进一步深入研究疫情与个体行为的相互反馈机制,提出和评价干预措施。研究结果有助于评估疾病干预策略,为疫情期间的交通政策提供决策支持,改善现有系统的不足。

    Abstract
    The emergence of novel infectious diseases has become a serious global problem. Convenient transportation networks lead to rapid mobilization in the context of globalization, which is an important factor underlying the rapid spread of infectious diseases. Transportation systems can cause the transmission of viruses during the epidemic period, but they also support the reopening of economies after the epidemic. Understanding the mechanism of the impact of mobility on the spread of infectious diseases is thus important, as is establishing the risk model of the spread of infectious diseases in transportation networks. In this study, the basic structure and application of various epidemic spread models are reviewed, including mathematical models, statistical models, network-based models, and simulation models. The advantages and limitations of model applications within transportation systems are analyzed, including dynamic characteristics of epidemic transmission and decision supports for management and control. Lastly, research trends and prospects are discussed. It is suggested that there is a need for more in-depth research to examine the mutual feedback mechanism of epidemics and individual behavior, as well as the proposal and evaluation of intervention measures. The findings in this study can help evaluate disease intervention strategies, provide decision supports for transport policy during the epidemic period, and ameliorate the deficiencies of the existing system.

    1. Modelling Strong Control Measures for Epidemic Propagation With Networks—A COVID-19 Case Study
      Publisher: IEEE
      Page(s): 109719 - 109731
      Date of Publication: 10 June 2020
      Electronic ISSN: 2169-3536
      INSPEC Accession Number: 19799178
      DOI: 10.1109/ACCESS.2020.3001298
      Publisher: IEEE
      被引用次数:16

    摘要:
    我们证明,建立一个疾病传播的信息模型不需要精确的传染病传播参数知识。我们提出了一个详细的模型,在各种外部控制制度下的接触网拓扑结构,并证明这足以捕捉显著的动态特性和通知决策。社区中个体之间的接触以接触图为特征,选择接触图的结构来模拟社区控制措施。我们的传染源城市级传播模型(SEIR模型)的特点是通过(a)无标度接触网(无控制);(b)随机图(消除大规模聚集);(c)小世界晶格(部分到完全封锁-“社会”距离)传播。该模型在模拟和2020年新型冠状病毒大流行传播的数据之间表现出良好的定性一致性。我们得到了SEIR模型的相关速率参数的估计,并证明了在这些估计的不确定性下我们的模型预测的鲁棒性。我们确定了这项工作的社会背景和效用,有助于在西澳大利亚进行高效的大流行应对。

    Abstract:
    We show that precise knowledge of epidemic transmission parameters is not required to build an informative model of the spread of disease. We propose a detailed model of the topology of the contact network under various external control regimes and demonstrate that this is sufficient to capture the salient dynamical characteristics and to inform decisions. Contact between individuals in the community is characterised by a contact graph, the structure of that contact graph is selected to mimic community control measures. Our model of city-level transmission of an infectious agent (SEIR model) characterises spread via a (a) scale-free contact network (no control); (b) a random graph (elimination of mass gatherings); and © small world lattice (partial to full lockdown-“social” distancing). This model exhibits good qualitative agreement between simulation and data from the 2020 pandemic spread of a novel coronavirus. Estimates of the relevant rate parameters of the SEIR model are obtained and we demonstrate the robustness of our model predictions under uncertainty of those estimates. The social context and utility of this work is identified, contributing to a highly effective pandemic response in Western Australia.

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