helloybz / deepwalk-clone

My implementation of Deepwalk in PyTorch

Deepwalk - Clone

Clone implementation of deepwalk paper in PyTorch.

• Source code by the original author

Install

Requirements:

• python >= 3.9

Commmand:

pip install git+https://github.com/helloybz/deepwalk-clone.git

Usage

deepwalk --data_root PATH --output_root PATH --config_file PATH [--gpu] [--checkpoint_period=0 INT]

--data_root is supposed to be a directory which contains at least 2 files, V and E.
V contains list of node ids separated by \n.
E contains list of pairs of node id, separated by \n.
The pair of node should has a form like [source_node_id]\t[target_node_id.

Example

data_root/V

1
2
3
4
5

data_root/E

1\t2
1\t4
2\t5
3\t1

Config file

All of the hyper-parameters of deepwalk is controlled in this yaml file.
Below is an example of the config file.

n_dims: 128 # referred as d in the paper.
random_walker:
  steps_per_walk: 40 # referred as t in the paper.
  walks_per_node: 80 # referred as r in the paper.
skipgram:
  lr: 0.025
  window_size: 10 # referred as w in the paper.

Experiments

CORA dataset, a citation network, is used.
Identical hyper parameters introduced in the paper are used to both the original Deepwalk and the cloned Deepwalk.\

• 128 dimensions
• 40 steps per walk
• 80 walks per node
• 10 window size (10+10+1 for both directions.)

Loss graph

Multi-Label Classification

• Logistic regression is used as classifier.
• Train:Test ratio varies from 1:9 to 9:1.
• Metrics are averaged after 10 runs.
• There are 7 classes in CORA dataset.

Micro F1

Column name means the percentage of train split.

Method	10%	20%	30%	40%	50%	60%	70%	80%	90%
Deepwalk-Clone	0.219	0.242	0.282	0.335	0.347	0.342	0.360	0.377	0.352
Deepwalk	0.200	0.206	0.215	0.223	0.213	0.236	0.242	0.256	0.247

Macro F1

Column name means the percentage of train split.

Method	10%	20%	30%	40%	50%	60%	70%	80%	90%
Deepwalk-Clone	0.411	0.421	0.427	0.457	0.478	0.468	0.477	0.511	0.454
Deepwalk	0.135	0.138	0.144	0.132	0.122	0.136	0.128	0.125	0.108

deepwalk-clone's Issues

Fix main script

Add Experiment result section to README.md

Run Deepwalk-clone to Cora dataset

Visualize result of Deepwalk-clone to karate dataset

Adopt torch BigGraph

Track losses

• Track losses for every iteration and write them to a file as json.

Write README

Enable checkpointing

Fix package name in help messages.

usage: DeepWalk-Clone [-h] [--data_root DATA_ROOT] [--config_file CONFIG_FILE] [--output_root OUTPUT_ROOT] [--checkpoint_period CHECKPOINT_PERIOD] [--gpu]

optional arguments:
  -h, --help            show this help message and exit
  --data_root DATA_ROOT
                        Path to the data root directory.
  --config_file CONFIG_FILE
                        Path to the config file.
  --output_root OUTPUT_ROOT
                        Path to the output root directory.
  --checkpoint_period CHECKPOINT_PERIOD
                        Period of making checkpoint in epoch. 0 for no checkpoints.
  --gpu

Change 'Deepwalk-Clone' into 'deepwalk'

Write portfolio's content in korean

Run node label classification experiment

Change split token for edge file from ' ' to '\t'

Validating the implementation

• #16
• Do Experiments and Report scores
• Compare the scores with that of the original works.

Remove redundant criterion

CLI

Add ENG/KOR toggle button

Add new optimizer Adam

Error handling for bad formatted Edges during making Graph