https://github.com/zw-zhang/hope

This is a sample implementation of "Asymmetric Transitivity Preserving Graph Embedding"(KDD 2016).
https://github.com/zw-zhang/hope

asymmetric-transtivity gsvd high-order-proximity network-embedding network-representation-learning

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This is a sample implementation of "Asymmetric Transitivity Preserving Graph Embedding"(KDD 2016).

• Host: GitHub
• URL: https://github.com/zw-zhang/hope
• Owner: ZW-ZHANG
• Created: 2018-06-01T06:19:12.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-06-29T05:10:38.000Z (over 6 years ago)
• Last Synced: 2024-08-04T10:02:23.275Z (4 months ago)
• Topics: asymmetric-transtivity, gsvd, high-order-proximity, network-embedding, network-representation-learning
• Language: Matlab
• Homepage:
• Size: 3.91 KB
• Stars: 15
• Watchers: 0
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# HOPE

This is a sample implementation of "[Asymmetric Transitivity Preserving Graph Embedding](http://www.kdd.org/kdd2016/papers/files/rfp0184-ouA.pdf)"(KDD 2016).

### Requirements

```

MATLAB R2014a

``` 

### Usage

run `embed_main.m` with matlab

```

Input:

	A: N*N adjacency matrix (sparse)

	K: dimensionality of embedding space

	beta: decaying constant, default is 0.5 / spectral radius

    

Output:

    U: N*K left embedding matrix

	V: N*K right embedding matrix

	The high-order proximity (katz) matrix is approximated by U * V'

```

### Cite

If you find this code useful, please cite our paper:

```

@inproceedings{ou2016asymmetric,

  title={Asymmetric transitivity preserving graph embedding},

  author={Ou, Mingdong and Cui, Peng and Pei, Jian and Zhang, Ziwei and Zhu, Wenwu},

  booktitle={Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining},

  pages={1105--1114},

  year={2016},

  organization={ACM}

}

```