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https://github.com/aryanahadinia/dani
The official implementation of "DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property"
https://github.com/aryanahadinia/dani
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The official implementation of "DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property"
- Host: GitHub
- URL: https://github.com/aryanahadinia/dani
- Owner: AryanAhadinia
- Created: 2023-09-26T13:45:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-15T00:15:50.000Z (11 months ago)
- Last Synced: 2024-03-15T01:32:29.037Z (11 months ago)
- Language: C++
- Homepage:
- Size: 3.45 MB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DANI
This is an official implementation for paper titled "DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property".
## How to Run C++ Version
The default implementation of DANI is in C++, which is way faster than the python version due to C++ characteristics.
### Compile the Code
To compile the code, run the command below.
```bash
make
```
### Input Data Format
Only input required for the code is a file containing nodes names and cascades sorted in ascending order regarding infection timestamps. The input file contains two parts which are separated by a blank line:
1. The first $n$ lines of the file contain the name of nodes in the network. Each line contain `` and `` where node ids are integers starting from 0 to $n-1$.
```text
,
```
2. The next $m$ lines contains $m$ cascades, each on a line. Each cascade is represented by a sequence of `` and the corresponding `` of infection pairs where `` and `` are separated by a comma and pairs are separated by a semi-colon. The cascades are sorted in ascending order regarding infection timestamps.
```text
,;,;,;...;,
```
An example of input file is in file [`cascades.txt`](dataset/cascades.txt) which contains 10000 cascades generated on the network in [`dataset/groundtruth/network.txt`](dataset/groundtruth/network.txt).
### Run the Code
Here is the list of arguments that can be passed to the code:
- `-i:` which is the address of input file.
- `-Y:` which is the address of output file.
Run the code using following command:
```bash
./main -i: -Y:
```
To run the code on the example input file, run the command below:
```bash
./main -i:dataset/cascades.txt -Y:dataset/inferred/network.txt
```
### Output
The output of the model is in _Tab Separated Triples_ format. Each line contains a triple in format of $u\space v\space \alpha$ in which alpha is scored assigned to directed edge $(u,\space v)$ by the model. Note that edges are sorted in descending order.
## How to Run Python Version
DANI has also a python version. If you want to use the python version, follow the instructions in README.md in `python` directory.
## How to Run Evaluation
### Compiling OSLOM2
We use OSLOM2 [2] for the community detection. You have to download [OSLOM2](http://www.oslom.org/code/OSLOM2.tar.gz) and unpack it in the `./OSLOM2` and compile it to do the evaluation. You can use the script below to do these works.
```bash
wget http://www.oslom.org/code/OSLOM2.tar.gz
tar -xvzf OSLOM2.tar.gz
cd OSLOM2
chmod 744 compile_all.sh
./compile_all.sh
cd ..
```
Note: Compiling OSLOM may result in some errors but it is OK for our usage.
### Calculating Metrics
To do the evaluation, run the evaluation code as below.
```bash
cd evaluation
python3 main.py --gt --pr
```
Where `` is a path to a directory contatining a file named `network.txt` containing true underlying network and `` is a path to a directory contatining a file named `network.txt` containing inferred network. For example, you can use the following command to run the evaluation on the example dataset.
```bash
cd evaluation
python3 main.py --gt ../dataset/groundtruth --pr ../dataset/inferred
```
## Compatibility
This code has been compiled and tested on Ubuntu 22.04 LTS using Python 3.10.
## Cite us
```bibtex
@misc{DANI,
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},
year={2023},
eprint={2310.01696},
archivePrefix={arXiv},
primaryClass={cs.SI}
}
```
## References
We have used SNAP library and OSLOM2 to develop this method.
[1] Leskovec, J. and Sosič, R., 2016. Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1), pp.1-20.
[2] Lancichinetti, A., Radicchi, F., Ramasco, J.J. and Fortunato, S., 2011. Finding statistically significant communities in networks. PloS one, 6(4), p.e18961.