A framework of graph classification baselines which including TUDataset Loader, GNN models and visualization.
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Datasets
Implemented based on LegacyTUDataset of dgl.data. All TUDataset is avalible. It will download auotomatically when using.
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GNN models
Including GCN, GAT, Gated GCN, GIN, GraphSAGE, etc. Deafult configurations can be find in
configs/.Models with configs having 500k trainable parameters
Rank Model #Params Test Acc ± s.d. Links 1 GatedGCN-PE 505421 86.363 ± 0.127 Paper 2 RingGNN 504766 86.244 ± 0.025 Paper 3 MoNet 511487 85.582 ± 0.038 Paper 4 GatedGCN 502223 85.568 ± 0.088 Paper 5 GIN 508574 85.387 ± 0.136 Paper 6 3WLGNN 502872 85.341 ± 0.207 Paper 7 GAT 526990 78.271 ± 0.186 Paper 8 GCN 500823 71.892 ± 0.334 Paper 9 GraphSage 502842 50.492 ± 0.001 Paper -
Visualization
All test on server 201 with python3.9 and CUDA 11.1. An environment is available at /home/jiaxing/anaconda3/envs/xjx.
pip install dgl-cu111 -f https://data.dgl.ai/wheels/repo.html
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
pip install matplotlib
pip install tenserboardX
python main.py --config configs/TUs_graph_classification_GCN_DD_100k.json
Output results are located in the folder defined by the variable out_dir in the corresponding config file.
If out_dir = 'out/TUs_graph_classification/', then
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Go to
out/TUs_graph_classification/results to view all result text files. -
Directory
out/TUs_graph_classification/checkpointscontains model checkpoints. -
To see the training logs in Tensorboard on local machine
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Go to the logs directory, i.e.
out/TUs_graph_classification/logs/. -
Run
tensorboard --logdir='./' --port 6006. -
Open
http://localhost:6006 in your browser. Note that the port information (here 6006 but it may change) appears on the terminal immediately after starting tensorboard.
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Add a class MyGraphLayer() in my_graph_layer.py file in the layers/ directory. A standard code is
import torch
import torch.nn as nn
import dgl
class MyGraphLayer(nn.Module):
def __init__(self, in_dim, out_dim, dropout):
super().__init__()
# write your code here
def forward(self, g_in, h_in, e_in):
# write your code here
# write the dgl reduce and updates function call here
return h_out, e_out
Directory layers/ contains all layer classes for all graph networks and standard layers like MLP for readout layers.
As instance, the GCN class GCNLayer() is defined in the layers/gcn_layer.py file.
Add a class MyGraphNetwork() in my_gcn_net.py file in the net/ directory. The loss() function of the network is also defined in class MyGraphNetwork().
import torch
import torch.nn as nn
import dgl
from layers.my_graph_layer import MyGraphLayer
class MyGraphNetwork(nn.Module):
def __init__(self, in_dim, out_dim, dropout):
super().__init__()
# write your code here
self.layer = MyGraphLayer()
def forward(self, g_in, h_in, e_in):
# write your code here
# write the dgl reduce and updates function call here
return h_out
def loss(self, pred, label):
# write your loss function here
return loss
Add a name MyGNN for the proposed new graph network class in load_gnn.py file in the net/ directory.
from nets.my_gcn_net import MyGraphNetwork
def MyGNN(net_params):
return MyGraphNetwork(net_params)
def gnn_model(MODEL_NAME, net_params):
models = {
'MyGNN': MyGNN
}
return models[MODEL_NAME](net_params)
For example, GCNNet() in nets/gcn_net.py is given the GNN name GCN in nets/load_net.py.
Benchmarking Graph Neural Networks [paper] [Github] https://codechina.csdn.net/mirrors/dmlc/dgl/-/tree/master/examples/pytorch