Implementation of FSGNN

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Deep Learning FSGNN
Overview

FSGNN

Implementation of FSGNN. For more details, please refer to our paper Experiments were conducted with following setup:
Pytorch: 1.6.0
Python: 3.8.5
Cuda: 10.2.89 Trained on NVIDIA V100 GPU.

Summary of results

Dataset 3-hop Accuracy(%) 8-hop Accuracy(%) 16-hop Accuracy(%) 32-hop Accuracy(%)
Cora 87.73 87.93 87.91 87.83
Citeseer 77.19 77.40 77.46 77.46
Pubmed 89.73 89.75 89.60 89.63
Chameleon 78.14 78.27 78.36 78.53
Wisconsin 88.43 87.84 88.04 88.24
Texas 87.30 87.30 86.76 86.76
Cornell 87.03 87.84 86.76 88.11
Squirrel 73.48 74.10 73.95 74.15
Actor 35.67 35.75 35.25 35.22
Actor(no-norm) 37.63 37.75 37.67 37.62

To run node classification on different hops:

3-hop : ./run_classification_3_hop.sh

8-hop : ./run_classification_8_hop.sh

16-hop : ./run_classification_16_hop.sh

32-hop : ./run_classification_32_hop.sh

In addition, we include model accuracy of Actor dataset without using hop-normalization, as model shows higher accuracy in this setting.

Some more interesting results coming soon!

ogbn-papers100M (large-scale dataset)

Improved model than mentioned in paper with extra FC layer. Please run python process_large.py in folder named large_data first to create data splits.

Then run ./run_ogbn_papers.sh to train the model.

Accuracy shown below is average over five runs with random seeds 0-4.

Dataset 4-hop Accuracy (%)
ogbn-papers100M 68.07

(Results may vary slightly with a different platform, e.g. use of different GPU. In such case, for best performance, some hyperparameter search may be required. Please refer to PyTorch documentation for more details.)

Datasets and parts of preprocessing code were taken from Geom-GCN and GCNII repositories. We thank the authors of these papers for sharing their code.

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Comments
  • chameleon datasets runing error

    chameleon datasets runing error

    I encountered a running problem. When I ran the chameleons dataset, "valueerror: integers to negative integer powers are not allowed." In line 87 of "process. Py". Is this my personal problem? And how to solve this problem. Thank you very much for your reply!

    Dataset: chameleon Dropout:0.5, layer_norm: True w_att:0.0005, w_fc2:0.0005, w_fc1:0.0005, lr_fc:0.02, lr_att:0.02 E:\python_project\FSGNN\process.py:143: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0. adj = nx.adjacency_matrix(G, sorted(G.nodes())) Traceback (most recent call last): File "E:/python_project/FSGNN/node_class.py", line 161, in accuracy_data = train(datastr,splitstr) File "E:/python_project/FSGNN/node_class.py", line 91, in train adj, adj_i, features, labels, idx_train, idx_val, idx_test, num_features, num_labels = full_load_data(datastr,splitstr) File "E:\python_project\FSGNN\process.py", line 148, in full_load_data features = preprocess_features(features) File "E:\python_project\FSGNN\process.py", line 87, in preprocess_features r_inv = np.power(rowsum, -1).flatten() ValueError: Integers to negative integer powers are not allowed.

    opened by shikexuan666 4
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