https://github.com/aditya-grover/node2vec

https://github.com/aditya-grover/node2vec

Last synced: 25 days ago
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• Host: GitHub
• URL: https://github.com/aditya-grover/node2vec
• Owner: aditya-grover
• License: mit
• Created: 2016-06-28T02:39:51.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-07-21T14:37:25.000Z (over 2 years ago)
• Last Synced: 2024-10-01T21:41:06.046Z (about 1 month ago)
• Language: Scala
• Homepage: http://snap.stanford.edu/node2vec/
• Size: 50.8 KB
• Stars: 2,613
• Watchers: 63
• Forks: 913
• Open Issues: 95
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# node2vec

This repository provides a reference implementation of *node2vec* as described in the paper:


> node2vec: Scalable Feature Learning for Networks.


> Aditya Grover and Jure Leskovec.


> Knowledge Discovery and Data Mining, 2016.


> 

The *node2vec* algorithm learns continuous representations for nodes in any (un)directed, (un)weighted graph. Please check the [project page](https://snap.stanford.edu/node2vec/) for more details. 

### Basic Usage

#### Example

To run *node2vec* on Zachary's karate club network, execute the following command from the project home directory:


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

#### Options

You can check out the other options available to use with *node2vec* using:


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

#### Input

The supported input format is an edgelist:

	node1_id_int node2_id_int 

		

The graph is assumed to be undirected and unweighted by default. These options can be changed by setting the appropriate flags.

#### 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 *node2vec*.

### Citing

If you find *node2vec* useful for your research, please consider citing the following paper:

	@inproceedings{node2vec-kdd2016,

	author = {Grover, Aditya and Leskovec, Jure},

	 title = {node2vec: Scalable Feature Learning for Networks},

	 booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},

	 year = {2016}

	}

### 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 *node2vec* algorithm and could benefit from several performance enhancement schemes, some of which are discussed in the paper.