Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterised by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource and represents a 61.5% increase in spatially-referenced real world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyse both real and synthetic networks using structural, spatial and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalisability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups using nine optimal graph metrics using K-means. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies.

翻译：拓扑基准是一个全面的拓扑数据集，旨在加速光网络领域的基准测试研究。该数据集聚焦于核心光网络，包含公开可访问且可直接使用的拓扑结构，具体涵盖：(a) 105个地理参照的真实世界光网络，以及(b) 270,900个经验证的合成拓扑。以往针对真实核心光网络的研究存在开放数据源碎片化与独立研究分散化的特点。此外，先前工作未能提供与本研究规模相当的合成数据集。拓扑基准通过提供统一资源解决了这一局限，并将具有空间参照的真实光网络数量提升了61.5%。为通过图论分析视角建立基准并揭示光网络拓扑的基本特性，我们采用结构、空间与谱度量指标对真实及合成网络进行分析。对比研究表明真实光网络多样性存在约束，并阐明合成网络如何补充和扩展可用拓扑的范围。当前拓扑选择多基于主观标准（如偏好、数据可用性或感知适用性），可能导致偏差且代表性有限。本框架通过提供更客观、系统化的拓扑选择方法，增强了光网络研究的普适性。统计与相关性分析揭示了所有图度量指标的定量范围及其相互关系。最后，我们应用无监督机器学习（K-means算法）基于九项最优图度量指标将真实拓扑聚类为特征鲜明的群组。分析结论部分提供了如何利用此类聚类为未来研究选择多样化拓扑集的指导方案。