geranio

<img src = './imgs/geranio-cover.png' width = 90%>

GERANIO is a comprehensive toolkit designed to facilitate the application, visualization and analysis of graph evolution rules algorithms. Graph evolution rules capture interpretable patterns describing the transformation of a small subgraph into a new subgraph, providing valuable insights into evolutionary behaviors. This repository provides a set of modules to help you prepare input data in the right format, run algorithms, read the output, and analyze and visualize the results.

Key Features

The following pipeline describes the process:

<img src = './imgs/pipeline.png' width = 90%>

• Input Preparation: GERANIO includes tools to streamline the process of formatting your input data to suit your analysis needs.

• Algorithm Execution: With GERANIO, you can easily run a variety of graph evolution algorithms to gain insights into the changes within your networks or graphs.

• Output Reading: The toolkit offers functions for reading and interpreting the results generated by the algorithms.

• Analysis and Visualization: GERANIO provides modules to help you analyze and visualize the evolution of graphs and networks over time. Understand the patterns, trends, and anomalies in your data.

Getting Started

To get started with GERANIO, you can read the introduction on the slides available <a href = 'https://drive.google.com/file/d/1DiQ48f2q3CRYKJ2qF_LhKmU74hD5Gnp8/view?usp=sharing'>here</a> and/or follow the process described in the jupyter notebook geranio.ipynb

You can also use a local version of the deep note project available here: <a href = 'https://deepnote.com/workspace/alessia-space-d070911a-c830-446b-96a0-7461fcb34d58/project/GERANIO-610e0907-6802-4939-b7da-d8a5a67aba28/notebook/geranio-7dec84036ec1460b9c8981dc15df58f2'>geraniodeepnote</a>

Citations

If you use the code or ideas in this repository for your research, please consider citing the following paper:

@inproceedings{galdeman2023unfolding,
  title={Unfolding temporal networks through statistically significant graph evolution rules},
  author={Galdeman, Alessia and Zignani, Matteo and Gaito, Sabrina},
  booktitle={2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)},
  pages={1--10},
  year={2023},
  organization={IEEE}
}