Software

DANI

Fast, diffusion-aware network inference that preserves the topological structure of the recovered graph. Linear time, with a MapReduce version for large graphs.

Source code

DANI recovers the edges of a social network from the cascades observed on it, combining a Markov transition matrix built from cascade timing with a structural similarity between nodes. Unlike link-only inference methods it keeps modular structure, degree distribution, density and clustering coefficients close to the true graph.

Getting started

Clone the repository and follow its README to run DANI on your own cascade data or on the synthetic networks used in the paper.

How to cite

If you use DANI in your research, please cite:

@article{ramezani2024dani,
  title = {{DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property}},
  author = {Maryam Ramezani and Aryan Ahadinia and Erfan Farhadi and Hamid R. Rabiee},
  journal = {Scientific Reports},
  year = {2024},
  doi = {10.1038/s41598-024-82286-x},
  eprint = {2310.01696},
  archivePrefix = {arXiv},
  url = {https://www.nature.com/articles/s41598-024-82286-x}
}

Paper

DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property

Maryam Ramezani, Aryan Ahadinia, Erfan Farhadi, Hamid R. Rabiee

Scientific Reports, 2024Cited by 5

DOI Publisher arXiv Code

BibTeX

@article{ramezani2024dani,
  title = {{DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property}},
  author = {Maryam Ramezani and Aryan Ahadinia and Erfan Farhadi and Hamid R. Rabiee},
  journal = {Scientific Reports},
  year = {2024},
  doi = {10.1038/s41598-024-82286-x},
  eprint = {2310.01696},
  archivePrefix = {arXiv},
  url = {https://www.nature.com/articles/s41598-024-82286-x}
}