Subnetin

Subnetin is a network analysis framework designed to identify subnet boundaries within large-scale computer networks and simulated graphs. It provides algorithms and tooling to partition a network into subnets that respect connectivity, traffic patterns, and administrative boundaries. Subnetin can import various graph formats and export results for network design, monitoring, and security applications.

The term Subnetin blends subnet with inference. It was introduced in academic literature in the mid-2010s by researchers exploring scalable subnet identification in dynamic networks. Since then, open-source implementations emerged, offering modular components for data ingestion, clustering, and visualization.

Subnetin's core algorithms combine spectral clustering with modularity-based optimization and constrained flow-analysis to produce subnet boundaries. It supports incremental updates, enabling re-partitioning as the network evolves. The framework emphasizes scalability, with parallelized routines and GPU-accelerated computations, and provides Python interfaces as well as a command-line toolkit.

Applications include network design and optimization, segmentation for security zones, traffic engineering in data centers, and research on network topology. Limitations include sensitivity to data quality, the need for accurate graph representations, and varying performance on extremely large or highly dynamic networks. It is not a universal solution for all subnetting problems.

Related topics include graph partitioning, modularity optimization, and subnetting in IP networks. Further information is available in project documentation and user guides accompanying specific releases.