SmallWorldEige

SmallWorldEige is a cross-platform open-source software framework designed for simulating, analyzing, and visualizing networks that exhibit small-world properties. It emphasizes eigenvector-based analysis and dynamic processes on graphs, enabling researchers to model information diffusion, epidemic spread, and social dynamics in networks characterized by short path lengths and high clustering.

The project originated in the late 2010s as a collaboration between researchers at the Institute for Complex Systems and the University of Techne. The name combines the concept of small-world networks with eigenvector centrality, reflecting the software’s focus on analyzing node influence through eigenvectors.

SmallWorldEige provides a modular engine with graph generators, including Watts–Strogatz, Barabási–Albert, and Erdos–Rényi models, along with support for dynamic rewiring and process simulations such as SIR and rumor spread. It offers a Python API and a high-performance C++ core, plus visualization and data export in GraphML and JSON.

The project is hosted on a public repository and released under the MIT license. Documentation covers installation, API usage, and tutorials. Contributions are accepted through pull requests, and the maintainers provide issue tracking and versioned releases.

Users include university courses and research groups studying network diffusion and resilience. Proponents note its flexibility and extensibility, while some report a steep learning curve for newcomers. It remains one of several tools used to explore small-world phenomena and eigenvector-based metrics.