Matzner, Robin; Ahuja, Akanksha; Sadeghi Yamchi, Rasoul; Doherty, Michael; Beghelli, Alejandra; Savory, Seb; Bayvel, Polina; (2025) Topology Bench: systematic graph-based benchmarking for core optical networks. Journal of Optical Communications and Networking , 17 (1) pp. 7-27. 10.1364/jocn.534477.

Text Topology_Bench___UCL_and_Cambridge_Collaboration_Review_1_Submission.pdf - Accepted Version Access restricted to UCL open access staff until 10 December 2025. Download (7MB)

Abstract

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. It employs a two-step optimisation process: optimal features are selected by maximising feature uniqueness through Principal Component Analysis, and the optimal number of clusters is determined by maximising decision boundary distances via Support Vector Machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Zenodo and GitHub, promotes accessibility, consistency, and reproducibility.

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