https://github.com/leoribeiro/struc2vec

This repository provides a reference implementation of struc2vec.
https://github.com/leoribeiro/struc2vec

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This repository provides a reference implementation of struc2vec.

• Host: GitHub
• URL: https://github.com/leoribeiro/struc2vec
• Owner: leoribeiro
• License: mit
• Created: 2017-01-16T15:35:54.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2023-07-09T17:46:21.000Z (over 1 year ago)
• Last Synced: 2024-08-02T13:22:32.789Z (5 months ago)
• Topics: algorithm, graph
• Language: Python
• Homepage:
• Size: 18.9 MB
• Stars: 381
• Watchers: 13
• Forks: 115
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: license.md

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README

        
# struc2vec

This repository provides a reference implementation of *struc2vec* as described in the paper:


> struc2vec: Learning Node Representations from Structural Identity.


> Leonardo F. R. Ribeiro, Pedro H. P. Saverese, Daniel R. Figueiredo.


> Knowledge Discovery and Data Mining, SigKDD, 2017.


The *struc2vec* algorithm learns continuous representations for nodes in any graph. *struc2vec* captures structural equivalence between nodes.  

Before to execute *struc2vec*, it is necessary to install the following packages:




``pip install futures``




``pip install fastdtw``




``pip install gensim``

### Update

Python 3 version: https://github.com/sebkaz/struc2vec/tree/master

### Basic Usage

#### Example

To run *struc2vec* on Mirrored Zachary's karate club network, execute the following command from the project home directory:


	``python src/main.py --input graph/karate-mirrored.edgelist --output emb/karate-mirrored.emb``

#### Options

To activate optimization 1, use the following option:

``--OPT1 true``




To activate optimization 2:

``--OPT2 true``




To activate optimization 3:

``--OPT3 true``




To run *struc2vec* on Barbell network, using all optimizations, execute the following command from the project home directory:




``python src/main.py --input graph/barbell.edgelist --output emb/barbell.emb --num-walks 20 --walk-length 80 --window-size 5 --dimensions 2 --OPT1 True --OPT2 True --OPT3 True --until-layer 6``

You can check out the other options available to use with *struc2vec* using:


	``python src/main.py --help``

#### Input

The supported input format is an edgelist:

	node1_id_int node2_id_int

		

#### Output

The output file has *n+1* lines for a graph with *n* vertices. 

The first line has the following format:

	num_of_nodes dim_of_representation

The next *n* lines are as follows:

	

	node_id dim1 dim2 ... dimd

where dim1, ... , dimd is the *d*-dimensional representation learned by *struc2vec*.

### Miscellaneous

Please send any questions you might have about the code and/or the algorithm to .

*Note:* This is only a reference implementation of the framework *struc2vec*.