https://github.com/rmanluo/gsim
Official code implementation of "GSim: A Graph Neural Network Based Relevance Measure for Heterogeneous Graphs"
https://github.com/rmanluo/gsim
Last synced: 29 days ago
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Official code implementation of "GSim: A Graph Neural Network Based Relevance Measure for Heterogeneous Graphs"
- Host: GitHub
- URL: https://github.com/rmanluo/gsim
- Owner: RManLuo
- Created: 2023-05-07T13:06:54.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-07-08T09:47:26.000Z (almost 2 years ago)
- Last Synced: 2024-05-22T00:06:30.262Z (about 1 year ago)
- Language: Python
- Size: 25.9 MB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Gsim
----
Official code implementation for TKDE paper GSim: A Graph Neural Network Based Relevance Measure for Heterogeneous Graphs
> L. Luo, Y. Fang, M. Lu, X. Cao, X. Zhang and W. Zhang, "GSim: A Graph Neural Network Based Relevance Measure for Heterogeneous Graphs," in IEEE Transactions on Knowledge and Data Engineering, doi: https://doi.org/10.1109/TKDE.2023.3271425.
## Environments
python == 3.6
CPU: I7-8700K
RAM: 64GB
GPU: RTX 3080
CUDA: 10.1
## Requirements
```bash
torch==1.6.0
torch-cluster==1.5.7
torch-scatter==2.0.5
torch-sparse==0.6.7
torch-spline-conv==1.2.0
torch_geometric==1.6.1
matplotlib==2.2.3
networkx==2.4
dgl==0.4.3.post2
numpy==1.16.6
scipy==1.4.1
scikit-learn==0.24.2
pyclustering == 0.10.1.2
```
## Config
```
vi config.py
```
config.py example
```python
import os
config_path = os.path.dirname(__file__)
data_config = {
'data_path': os.path.join(config_path, 'data'),
'dataset': 'DBLP',
'data_name': 'DBLP.mat',
'primary_type': 'a',
'query_type': 'a', # for recall evaluation
'return_type': 'a', # for recall evaluation
'query_name': '', # for recall evaluation
'task': ['CL'],
'K_length': 2,
'resample': False,
'random_seed': 123,
'train_val_test_ratio': [0.25, 0.25, 0.5]
}
model_config = {
'primary_type': data_config['primary_type'],
'auxiliary_embedding': 'linear', # auxiliary embedding generating method: non_linear, linear, embedding
'K_length': data_config['K_length'],
'embedding_dim': 128,
'in_dim': 128,
'out_dim': 128,
'num_heads': 2,
'merge': 'mean', # Multi head Attention merge method: linear, mean, stack
'g_agg_type': 'mean', # Graph representation encoder: mean, sum
'drop_out': 0.3,
'cgnn_non_linear': True, # Enable non linear activation function for CGNN
'multi_attn_linear': False, # Enable atten K/Q-linear for each type
'graph_attention': True,
'kq_linear_out_dim': 128,
'path_attention': False, # Enable Context path attention
'c_linear_out_dim': 8,
'enable_bilinear': False, # Enable Bilinear for context attention
'enable_relational_passing': True, # Enable Relational Message Passing
'gru': True,
'add_init': False,
'max_norm': 2, # Renorm, 0 for disable.
}
train_config = {
'continue': False,
'evaluate_every': -1,
'beta': 1,
'sup_weight': 1,
'unsup_weight': 1,
'lr': 0.001,
'l2': 0.1,
'factor': 0.2,
'total_epoch': 10000000,
'batch_size': 1024 * 20,
'pos_num_for_each_hop': [20, 20, 20, 20, 20, 20, 20, 20, 20],
'neg_num_for_each_hop': -1,
'sample_workers': 8,
'patience': 100,
'checkpoint_path': os.path.join(config_path, 'checkpoint', data_config['dataset'])
}
evaluate_config = {
'method': 'SpectralClustering', # 'kmeans', 'kmedoids', 'AgglomerativeClustering', 'SpectralClustering'
'use_sim_matrix': True,
'save_heat_map': False,
'result_path': os.path.join('result', data_config['dataset']),
'random_state': 123,
'max_iter': 500,
'n_jobs': 1,
'n_time': 100,
'eval_time': 50,
'K': 10 # number of results for recall exp.
}
```
## Train and Evaluate
``` bash
python3 main.py
```
## BibTex
```tex
@ARTICLE{10111040,
author={Luo, Linhao and Fang, Yixiang and Lu, Moli and Cao, Xin and Zhang, Xiaofeng and Zhang, Wenjie},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={GSim: A Graph Neural Network Based Relevance Measure for Heterogeneous Graphs},
year={2023},
volume={},
number={},
pages={1-14},
doi={10.1109/TKDE.2023.3271425}}
```