# graphwave

**Repository Path**: jjjxk/graphwave

## Basic Information

- **Project Name**: graphwave
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-23
- **Last Updated**: 2024-12-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GraphWave
## Spectral Wavelets for learning structural signatures in complex networks


This folder contains the code for GraphWave, an algorithm for computing structural signatures for nodes in a network using heat spectral wavelets. 
This code folder is organized as follows:

+ __shapes/__: contains the functions for generating (more or less) regular graphs and shapes
+ __performance_evaluation/__: functions computing different metrics for assessing the quality of the embeddings generated by GraphWave
+ __test\_perturbation\_synthetic/__: set of ipython notebooks for running the synthetic
experiments described in the paper.
+ __utils/__: set of  helper functions
+ files:
    + __characteristic\_functions.py__: functions for computing the characteristic functions.
    + __graphwave.py__: wrapper function for computing the embeddings.

 

 


### Prerequisites

GraphWave was written for __Python 2.7__ and requires the installation of the following Python libraries:


+ __networkx__: allows easy manipulation and plotting of graph objects (more information in the [Networkx website](https://networkx.github.io)).
+ __pyemd__: module for computing Earth Mover distances (for trying out other distances between diffusion distributions. More information in the [pyemd website](https://github.com/wmayner/pyemd))



Also, need standard packages: __scipy, sklearn, seaborn__ for analyzing and plotting results.

Note: heat diffusion scaling wavelets can also be computed with the Graph Signal Processing toolbox  __pygsp__ (accessible through the  [EPFL website](https://pygsp.readthedocs.io/en/stable/)  ), which, beyond structural similarities, has plenty of extremely useful features for handling signals on graphs.





 

 

## Running Graphwave

A full example on how to use GraphWave is provided in the ''Using GraphWave.ipynb" of this directory.
In a nutshell:

+ input: nx (or pygsp) Graph structure
+ compute the heat wavelets
+ embed the distributions in Euclidean space using the characteristic function
+ output: signatures, which can be used in one's favorite Machine Learning framework.

For a given graph __G__ (of type pygsp or nx),GraphWave structural signatures can be simply 
computing by calling:


```
>from graphwave import graphwave

>chi,heat_print, taus=graphwave_alg(G, 'automatic', verbose=False)

```






## Authors

* Anonymous at this time

## Acknowledgements

We would like to thank the authors of [struc2vec](https://arxiv.org/abs/1704.03165) for the [open access of the implementation of their method](https://github.com/leoribeiro/struc2vec), as well as [Lab41](http://lab41.github.io/blog/2014/12/18/rolx-discovering-individuals-roles-in-a-social-network/) for its open-access implementation of [RolX](https://dl.acm.org/citation.cfm?id=2339723).

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details



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# graphwave
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