Enhanced methods for approximating the structure of large networks

这个项目旨在通过发展复杂分析技术来解释现实世界网络(如互联网和在线社交网络)的基本结构特征。目前有关可靠性、稳健性和资源优化分配等属性的知识依赖于在实际应用中无效的假设。该项目希望通过将高阶近似的创新想法引入复杂的随机设置来提高对非均匀网络模型的理解。预期结果包括对满足给定约束的离散对象进行近似计数的新工具。这些工具的应用可能会对研究离散系统数量特征的研究人员产生深远的好处。

This project aims to explain fundamental structural features of real-world networks such as the internet and online social networks, by advancing complex-analytical techniques. Current knowledge of properties such as reliability, robustness and optimal allocation of resources rely on assumptions that are invalid in real applications. The project expects to improve understanding of inhomogeneous network models by introducing an innovative idea of high-order approximations to complex random settings. Expected outcomes include new tools for approximate counting of discrete objects satisfying given constraints. Applications of these tools could have far-reaching benefits to researchers who study quantitative characteristics of discrete systems.