复杂网络研究正渗透到数理学科、生命学科和工程学科等众多不同的领域，对复杂网络的定量与定性特征的科学理解已成为网络时代科学研究中一个极其重要的挑战性课题。本书致力于系统地介绍复杂网络的基础知识和研究进展。由于复杂网络研究具有很强的跨学科特色，并且新的问题和研究成果不断涌现，因此本书着眼于复杂网络研究中已经取得的主要研究进展。主要内容包括： 网络拓扑特性与模型，复杂网络上的传播行为、相继故障、搜索算法和社团结构，以及复杂网络的同步与控制。本书还详细介绍了作者们在复杂网络领域的有关工作。.

通信中的流量识别工作将直接影响网络管理整体效率. 针对复杂网络中的加密流量识别问题,结合网络流 量与文本结构相似性,提出一种基于卷积神经网络的优化识别模型. 综合考虑数据包的多样性,对原始网络数据进 行预处理,以保证算法输入数据结构的一致性. 同时,算法增加卷积操作,以提高模型特征提取效率. 仿真结果表 明,提出的卷积神经网络模型在复杂网络环境中,加密流量的服务识别与应用识别都有较高准确率. 关键词:流量识别;复杂网络;加密;卷积神经网络;预处理;特征提取

复杂网络的链路预测

在基于节点的攻击引发的级联故障中识别复杂网络中的易受攻击的节点

Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems, metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging pre- sentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, mathematics, engineering, biology, neuroscience and social sciences.

Here you find all the data sets described and analysed in the textbook: "Complex Networks: Principles, Methods and Applications", V. Latora, V. Nicosia, G. Russo (Cambridge University Press, 2017) For each data set you find below a brief description and a list of salient properties (number of node, number of edges, etc.). All data sets All the data sets of the textbook are available for download in a single compressed file: All the data sets in the book (zip) The archive contains one folder for each dataset, The file README.txt in each folder contains some relevant information about the corresponding data set.

复杂网络中基本网络模型的matlab实现.rar 随机网络，小世界网络以及无标度网络的构建算法。计算聚类系数和平均路径长度

该程序可以生成无标度网络，并可以计算网络的度分布及聚集系数．复杂网络 complex network基本参数计算 边权重计算 根据定义不同 可以选择不同的计算方法可变聚类无标度网络源代码

ER随机网络 当p不太小时， 聚集系数较小， 平均最短路径较短。

NetLogo是一个用来对自然和社会现象进行仿真的可编程建模环境。它是由Uri Wilensky在1999年发起的，由连接学习和计算机建模中心（CCL）负责持续开发。