https://github.com/tadpole/DRNE

The Implementation of "Deep Recursive Network Embedding with Regular Equivalence"(KDD 2018)
https://github.com/tadpole/DRNE

centrality deep-neural-networks network-embedding network-representation-learning

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The Implementation of "Deep Recursive Network Embedding with Regular Equivalence"(KDD 2018)

• Host: GitHub
• URL: https://github.com/tadpole/DRNE
• Owner: tadpole
• Created: 2018-10-31T11:05:37.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-05-21T14:53:18.000Z (over 5 years ago)
• Last Synced: 2024-08-02T13:21:51.570Z (5 months ago)
• Topics: centrality, deep-neural-networks, network-embedding, network-representation-learning
• Language: Python
• Homepage: http://thumedialab.com/Deep-Recursive-Network-Embedding-with-Regular-Equivalence
• Size: 968 KB
• Stars: 26
• Watchers: 4
• Forks: 15
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-graph-based-semi-supervised-learning - code
• graph-networks - Deep Recursive Network Embedding with Regular Equivalence

README

        
# DRNE

The Implementation of "[Deep Recursive Network Embedding with Regular Equivalence](https://dl.acm.org/citation.cfm?doid=3219819.3220068)"(KDD 2018).

### Requirements

```

Python >= 3.5.2

scipy >= 0.19.1

numpy >= 1.13.1

tensorflow == 1.2.0

networkx >= 1.11

```

### Usage

##### Example Usage

```

python src/main.py --data_path dataset/barbell.edgelist --save_path result/barbell --save_suffix test \

      -s 16 -b 256 -lr 0.0025 --index_from_0 True

```

##### Full Command List

```

usage: Deep Recursive Network Embedding with Regular Equivalence

       [-h] [--data_path DATA_PATH] [--save_path SAVE_PATH]

       [--save_suffix SAVE_SUFFIX] [-s EMBEDDING_SIZE] [-e EPOCHS_TO_TRAIN]

       [-b BATCH_SIZE] [-lr LEARNING_RATE] [--undirected UNDIRECTED]

       [-a ALPHA] [-l LAMB] [-g GRAD_CLIP] [-K K]

       [--sampling_size SAMPLING_SIZE] [--seed SEED]

       [--index_from_0 INDEX_FROM_0]

optional arguments:

  -h, --help            show this help message and exit

  --data_path DATA_PATH

                        Directory to load data.

  --save_path SAVE_PATH

                        Directory to save data.

  --save_suffix SAVE_SUFFIX

                        Directory to save data.

  -s EMBEDDING_SIZE, --embedding_size EMBEDDING_SIZE

                        the embedding dimension size

  -e EPOCHS_TO_TRAIN, --epochs_to_train EPOCHS_TO_TRAIN

                        Number of epoch to train. Each epoch processes the

                        training data once completely

  -b BATCH_SIZE, --batch_size BATCH_SIZE

                        Number of training examples processed per step

  -lr LEARNING_RATE, --learning_rate LEARNING_RATE

                        initial learning rate

  --undirected UNDIRECTED

                        whether it is an undirected graph

  -a ALPHA, --alpha ALPHA

                        the rate of structure loss and orth loss

  -l LAMB, --lamb LAMB  the rate of structure loss and guilded loss

  -g GRAD_CLIP, --grad_clip GRAD_CLIP

                        clip gradients

  -K K                  K-neighborhood

  --sampling_size SAMPLING_SIZE

                        sample number

  --seed SEED           random seed

  --index_from_0 INDEX_FROM_0

                        whether the node index is from zero

```

### Cite

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

```

@inproceedings{tu2018deep,

  title={Deep recursive network embedding with regular equivalence},

  author={Tu, Ke and Cui, Peng and Wang, Xiao and Yu, Philip S and Zhu, Wenwu},

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

  pages={2357--2366},

  year={2018},

  organization={ACM}

}

```