GraphPoolingGarden

A repo for baseline of graph pooling methods.

中文

Datasets

TuDataset
- D&D
- PROTEINS
- ENZYMES
- NCI1/NCI109
- Reddit-Binary
OGB
- ogbg-molhiv
- ogbg-ppa
- ogbg-code2

Pooling methods

set2set
sagpool(sequence/hierarchical)
graph-U-net
Diffpool

Model Framework

hierarchical model
sequencial model
U-net-like model: only for graph U-net model
diffpooling model: only for diffpool model

Readout methods

mean
max
sum
set2set

ConvLayer

GCN: layers/gcn_layer.py
GIN: layers/gin_layer.py
GraphSAGE:layers/graphsage_layer.py

Usage

1. create the config json file

create a config.json in configs folder, an example is like this:

{
    "dataset_name": ["ENZYMES"],
    "batch_size": 2,
    "epochs": 100,
    "seed": [1,2,3,4,5],
    "model":"global",
    "gnn_type": "gcn",
    "num_layer": 3,
    "emb_dim": 300,
    "drop_ratio": 0.5,
    "virtual_node": "False",
    "residual": "False",
    "JK": "last",
    "pooling": "sagpool",
    "sagpool": {
        "keep_ratio": 0.8,
        "activation": "tanh",
        "layer_num": 1
    }
}

When the key is a list such as dataset_name, all permutation will be trained and log in the csv file named as the comabination of the key values of 'dataset_name' and 'pooling'. In the examle, the final result will be saved in ENZYMES_sagpool.csv.

Warn:

Not all values can be written list-like, only the following keys can:

dataset_name
seed

2. running train.py to train, valid and test

python graphpoolinggarden/train.py --config configs/config.json

Acknowledgement

The program is built based on the code of the following released codes and programs:

We appreciate the authors' effort for the contribution to the research community.

codingClaire/GraphPoolingGarden

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