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

This is the official implementation of "Arbitrary-Order Proximity Preserved Network Embedding"(KDD 2018).
https://github.com/zw-zhang/arope

arbitrary-order-proximity high-order-proximity network-embedding network-representation-learning svd

Last synced: about 1 month ago
JSON representation

This is the official implementation of "Arbitrary-Order Proximity Preserved Network Embedding"(KDD 2018).

• Host: GitHub
• URL: https://github.com/zw-zhang/arope
• Owner: ZW-ZHANG
• Created: 2018-06-01T06:00:41.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-02T08:02:27.000Z (over 5 years ago)
• Last Synced: 2024-11-01T00:32:20.703Z (about 2 months ago)
• Topics: arbitrary-order-proximity, high-order-proximity, network-embedding, network-representation-learning, svd
• Language: Python
• Homepage:
• Size: 1.67 MB
• Stars: 38
• Watchers: 0
• Forks: 11
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# AROPE

This is the official implementation of "[Arbitrary-Order Proximity Preserved Network Embedding](http://cuip.thumedialab.com/papers/NE-ArbitraryProximity.pdf)"(KDD 2018).

We provide two implementations: MATLAB and Python. Note that the MATLAB version is faster in our testing and is used in producing original results in the paper.

### Requirements

```

MATLAB R2017a

or 

Python >= 3.5.2

numpy >= 1.14.2

scipy >= 1.0.0

pandas >= 0.22.0

``` 

### Usage

#### Main Function

```

[U_output, V_output] = AROPE(A,d,order,weights)

```

```

Input:

    A: sparse adjacency matrix or its variations, must be symmetric

    d: dimensionality 

    order: 1 x r vector, order of the proximity

    weights: 1 x r cell/list, each containing the weights for one high-order proximity

Output:

    U_output/V_output: 1 x r cell/list, each containing one content/context embedding vectors 

```

#### Example Usage

See SampleRun.m or SampleRun.py for a sample run of network reconstruction on BlogCatalog dataset

### Cite

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

```

@inproceedings{zhang2018arbitrary,

  title={Arbitrary-Order Proximity Preserved Network Embedding},

  author={Zhang, Ziwei and Cui, Peng and Wang, Xiao and Pei, Jian and Yao, Xuanrong and Zhu, Wenwu},

  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},

  pages={2778--2786},

  year={2018},

  organization={ACM}

}

```