Enhanced methods for approximating the structure of large networks

该项目旨在通过推进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。目前的知识，如可靠性，鲁棒性和资源的最佳分配的属性依赖于假设，是无效的真实的应用程序。该项目希望通过引入高阶近似复杂随机设置的创新思想来提高对非齐次网络模型的理解。预期成果包括满足给定约束条件的离散物体近似计数的新工具。这些工具的应用可能对研究离散系统定量特征的研究人员产生深远的影响。

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.