FangShuheng/CGNP

Community Search: A Meta-Learning Approach

A PyTorch + torch-geometric implementation of CGNP, as described in the paper: Shuheng Fang, Kangfei Zhao, Guanghua Li, Jeffery Xu Yu. [Community Search: A Meta-Learning Approach]

Requirements

python 3.8
networkx
numpy
scipy
scikit-learn
torch 1.7.1
torch-geometric 1.7

Import the conda environment by running

conda env create -f cgnp.yaml
conda activate cgnp
pip install -r requirements.txt

Quick Start

Running Facebook

python main.py    \
       --data_set facebook     \
       --gnn_type GAT     \
       --meta_method cnp      \
       --data_dir [your/own/directory/containing/facebook/dataset (i.e. /home/shfang/data/facebook/facebook)]  \
       --pool_type avg

Key Parameters

All the parameters with their default value are in get_args.py

name	type	description
num_layers	int	number of GNN layers
gnn_type	string	type of GNN layer (GCN, GAT, SAGE)
pool_type	string	type of the Commutative Operation (att, mean,sum)
meta_method	string	type of different meta learning algorithm
epochs	int	number of training epochs
query_node_num	int	total number of query nodes for one task
num_shots	int	number of query nodes of support set for one task
subgraph_size	int	size of subgraph sampled in large graph
data_set	string	dataset
task_num	int	number of training tasks
test_task_num	int	number of testing tasks
label_mode	string	community mode: shared community or disjoint community
num_pos	float	maximum proportion of positive instances for each query node
num_neg	float	maximum proportion of negative instances for each query node
learning_rate	Float	learning rate

Project Structure

main.py         # project extrance
util.py         # generate tasks for different dataset, evaluation
QueryDataset.py # extract query from subgraphs and generate support/query set for meta algorithm
get_args.py     # parameters settings

    /meta
        cnp.py                       # train and test for CGNP
    /model
        FwLayer.py
        Model.py                      # model for CGNP
        Layer.py                      # GNN layers and other layers
        Loss.py

To use your own dataset, you can put the data graphs, feature, ground truth communities to '/data/disjoint(shared)/DATASET_NAME/graph_dgl.pkl', '/data/disjoint(shared)/DATASET_NAME/features.npy', '/data/disjoint(shared)/DATASET_NAME/label.pkl', respectively. And to preprocess, divide the graph into two parts, train.csv and test.csv, you can refer to citation_preprocess.py.

The format of input graph Arxiv/Cora/Citeseer and feature follows G-Meta and you can also get Arxiv dataset from it; The Reddit/Cora/Citeseer datasets are from torch-geometric; For DBLP, you can download it in [SNAP] (https://snap.stanford.edu/data/com-DBLP.html); For Facebook, find it in [SNAP] (https://snap.stanford.edu/data/ego-Facebook.html).

Contact

Open an issue or send email to shfang@se.cuhk.edu.hk if you have any problem

Cite Us

@inproceedings{fang2023community,
  title={Community search: a meta-learning approach},
  author={Fang, Shuheng and Zhao, Kangfei and Li, Guanghua and Yu, Jeffrey Xu},
  booktitle={2023 IEEE 39th International Conference on Data Engineering (ICDE)},
  pages={2358--2371},
  year={2023},
  organization={IEEE}
}

FangShuheng / CGNP