Machine Learning for Complex Networks

复杂网络的机器学习

• 批准号: RGPIN-2018-06868
• 负责人: Trajkovic, Ljiljana
• 金额: $ 4.08万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752993
• 关键词: Machine; Learning; Complex; Networks

My proposed research program focuses on developing methods and tools for improving performance of complex networks, including data communication and social networks. The program encompasses analysis of data collected from deployed networks, characterization and modeling of network traffic, and development of software tools for evaluating network performance.My overall objective is to develop formal analytical and statistical methods for analysis of complex networks. I will employ spectral graph theory and machine learning to analyze various topologies of complex social networks such as Facebook, LinkedIn, Twitter, and their dynamical behavior. This analysis will capture historical trends in the development of these networks. Machine learning techniques and algorithms will be also used to classify network anomalies in complex data networks. Training and testing of large collected datasets will be performed using the Compute Canada Cedar national system located at Simon Fraser University.I also plan to apply machine learning techniques for developing new algorithms for resource allocations in software-defend networks (SDN) and network functions virtualization (NFV). The virtualized network architecture enables coexistence of multiple virtual networks on an existing physical infrastructure. The Virtual Network Embedding (VNE) problem, which deals with the embedding of virtual network components onto a physical network, is known to be non-deterministic polynomial-time (NP)-hard. Hence, of particular interest and challenge is the development of VNE algorithms for wireless networks where the network topology varies with time. Performance of the developed network algorithms will be evaluated using our recently developed VNE-Sim software platform that enables definition and implementation of various network elements and generation of various network topologies.Research results emanating from the proposed program will improve our understanding of the underlying mechanisms that govern the behavior of the Internet and social networks. They will enhance network security and help improve network performance. This is of fundamental importance to Canadian telecommunications industry and to Canadian network service providers. A library of developed software tools and models will be made publicly available to the research community.

我建议的研究计划侧重于开发方法和工具，以提高复杂网络的性能，包括数据通信和社交网络。该计划包括从部署的网络，网络流量的表征和建模收集的数据分析，并评估网络性能的软件工具的开发。我的总体目标是开发复杂网络分析的正式分析和统计方法。我将利用谱图理论和机器学习来分析Facebook、LinkedIn、Twitter等复杂社交网络的各种拓扑结构及其动态行为。这一分析将反映这些网络发展的历史趋势。机器学习技术和算法也将用于对复杂数据网络中的网络异常进行分类。大型收集数据集的训练和测试将使用位于西蒙弗雷泽大学的加拿大计算雪松国家系统进行。我还计划应用机器学习技术开发软件防御网络（SDN）和网络功能虚拟化（NFV）中资源分配的新算法。虚拟化网络架构使多个虚拟网络能够在现有物理基础设施上共存。虚拟网络嵌入（VNE）问题是一个非确定性的多项式时间（NP）难题，它涉及将虚拟网络组件嵌入到物理网络中。因此，特别感兴趣和挑战的是开发VNE算法的无线网络中的网络拓扑结构随时间变化。开发的网络算法的性能将使用我们最近开发的VNE-Sim软件平台进行评估，该软件平台可以定义和实现各种网络元素并生成各种网络拓扑结构。来自拟议计划的研究结果将提高我们对互联网和社交网络行为的基本机制的理解。它们将增强网络安全性，并有助于提高网络性能。这对加拿大电信业和加拿大网络服务提供商至关重要。将向研究界公开提供一个已开发的软件工具和模型库。