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Predicting graph structure from a time series of graphs

If you have a time series of dynamic graphs with changing structure, how would you predict future graphs? This is the goal of netseer.

Netseer predicts the graph structure including new nodes and edges from a time series of graphs. It adapts Flux Balance Analysis, a method used in metabolic network reconstruction to predict the structue of future graphs. The methodology is explained in the preprint (Kandanaarachchi et al. 2025).

R package

The algorithm is available in both R and Python. The R package netseer in on CRAN and can be installed as follows:

install_packages("netseer")

The vignette for the R package is available under [Get Started] at https://sevvandi.github.io/netseer/

Python package

The Python package is available from PyPI at https://pypi.org/project/netseer/. The Python package can be installed as follows:

pip install netseer

The documentation for the Python package is available at https://sevvandi.github.io/netseer-python/.

Coding Credits

A big shout out to Stefan Westerlund and Brodie Oldfield for their contributions. Stefan optimized the algorithm in R and coded it from scratch in Python. Brodie packaged up the Python code and did the website for the Python version.

References

Kandanaarachchi, Sevvandi, Ziqi Xu, Stefan Westerlund, and Conrad Sanderson. 2025. “Predicting Graph Structure via Adapted Flux Balance Analysis.” https://arxiv.org/abs/2507.05806.