https://github.com/xptree/NetMF

Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
https://github.com/xptree/NetMF

deepwalk matrix-factorization network-embedding node2vec social-network social-network-analysis

Last synced: 1 day ago
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Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

• Host: GitHub
• URL: https://github.com/xptree/NetMF
• Owner: xptree
• License: mit
• Created: 2017-11-27T05:40:39.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-01-27T13:08:39.000Z (almost 5 years ago)
• Last Synced: 2024-08-03T15:17:51.461Z (3 months ago)
• Topics: deepwalk, matrix-factorization, network-embedding, node2vec, social-network, social-network-analysis
• Language: Python
• Homepage:
• Size: 1.16 MB
• Stars: 186
• Watchers: 9
• Forks: 47
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-graph-based-semi-supervised-learning - code

README

        
# NetMF

This is a Python implementation of NetMF for the task of network embedding learning, as described in our paper:

 

[Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://arxiv.org/abs/1710.02971)

## Data Sets

BlogCatalog [Source](http://socialcomputing.asu.edu/datasets/BlogCatalog3) [Preprocessed](http://leitang.net/code/social-dimension/data/blogcatalog.mat)

Protein-Protein Interaction [Source](http://thebiogrid.org/download.php) [Preprocessed](http://snap.stanford.edu/node2vec/Homo_sapiens.mat)

Wikipedia [Source](http://www.mattmahoney.net/dc/textdata) [Preprocessed](http://snap.stanford.edu/node2vec/POS.mat)

[Flickr](http://leitang.net/code/social-dimension/data/flickr.mat)

## Cite

Please cite our paper if you use this code in your own work:

```

@inproceedings{qiu2018network,

  title={Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec},

  author={Qiu, Jiezhong and Dong, Yuxiao and Ma, Hao and Li, Jian and Wang, Kuansan and Tang, Jie},

  booktitle={Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining},

  pages={459--467},

  year={2018},

  organization={ACM}

}

```