【知识网络图】计算机网络汇总

可对拟定的项目进度计划实施有效监控，对计划实现动态管理。

项目范围说明书、需求矩阵、网络图、挣值分析、成本预算、绩效分析

数据中心机柜布局图，42U机柜布局图，布局直观且美观，各位网络工作中或者网络安全领域专家画图上墙必备模板。

一款免费的时间进度图表绘制工具，方便各种横道图、进度图、网络图、甘特图的制作，兼具打印功能

免费ospf理解LSA简单搭建的ensp拓扑图；可通过私信博主领取 https://blog.csdn.net/qq_43710889/article/details/116541738

This accessible book provides an introduction to the analysis and design of dynamic multiagent networks. Such networks are of great interest in a wide range of areas in science and engineering, including: mobile sensor networks, distributed robotics such as formation flying and swarming, quantum networks, networked economics, biological synchronization, and social networks. Focusing on graph theoretic methods for the analysis and synthesis of dynamic multiagent networks, the book presents a powerful new formalism and set of tools for networked systems. The book's three sections look at foundations, multiagent networks, and networks as systems. The authors give an overview of important ideas from graph theory, followed by a detailed account of the agreement protocol and its various extensions, including the behavior of the protocol over undirected, directed, switching, and random networks. They cover topics such as formation control, coverage, distributed estimation, social networks, and games over networks. And they explore intriguing aspects of viewing networks as systems, by making these networks amenable to control-theoretic analysis and automatic synthesis, by monitoring their dynamic evolution, and by examining higher-order interaction models in terms of simplicial complexes and their applications. The book will interest graduate students working in systems and control, as well as in computer science and robotics. It will be a standard reference for researchers seeking a self-contained account of system-theoretic aspects of multiagent networks and their wide-ranging applications. This book has been adopted as a textbook at the following universities: University of Stuttgart, Germany Royal Institute of Technology, Sweden Georgia Tech, USA University of Washington, USA Ohio University, USA

tikz-nef nef TikZ库提供了预定义的样式和形状，以创建使用神经工程框架（NEF）[1]的方法构建的神经网络图。 上面的示例是使用以下代码生成的： \begin { tikzpicture }[nef] \graph { input [ext] -> gate [ens] -> integrator/ $ x $ [ens] -> output [ext]; integrator -> [bend right, "$-1$" ] gate; integrator -> [recurrent] integrator; store -> [inhibit] gate; }; \end { tikzpicture } 支持以下样式： ea：合奏数组 ENS：合奏 ext：外部输入或输出 抑制：抑

利用python载入邻接矩阵绘制网络图，基于python语言的特点，对邻接矩阵加以处理后再进行应用，即将邻接矩阵去除第一列（节点序号列），复杂网络的基本拓扑结构可以用图论的方法表示成G =(V，E)，V中元素称为节点或顶点，E中元素称为边。在图论框架下, 可用不同的全局参量来表示复杂网络的特征, 最基本特征包括度分布 P(k)和聚类系数C。

Aruba无线网络基本配置指导手册.doc