Awesome

Codes for the paper "Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks", which is accepted to The IEEE International Conference on Data Mining (ICDM 2022). A full version of the paper (including proofs) can be found at: https://arxiv.org/abs/2102.06462.

Please cite us if you find our code useful:

To run the code:

Software Versions:

Python: 3.7.8

numpy: 1.18.5

scipy: 1.5.3

pytorch: 1.6.0

networkx: 2.5

scikit-learn: 0.23.2

dgl: it is only used to run baseline GeomGCN. If you need to run this baseline, you need to use this version of dgl: 0.4.3

If you do not need to run GeomGCN baseline, you can install any version of dgl or remove the the codes related to the GeomGCN baseline (in process.py and full-supervised.py).

To replicate the results of Table 1, you can using ./table_1_[model_name].sh to obatin the results of the specified model.

To replicate Table 2, you can still use hyparameters used in table_1_[model_name].sh and modify the layers.

To replicate Table B1, you can run ./table_B1.sh