readme.md

GPR-GNN

Paper link: https://arxiv.org/pdf/2006.07988.pdf
Author's code repo:https://github.com/jianhao2016/GPRGNN. Note that the original code is implemented with PyTorch for the paper.

Run with following (available dataset: "cora", "citeseer", "pubmed", "computers", "photo", "squirrel", "chameleon", "cornell", "texas")

sh Reproduce_GPRGNN.sh

	Cora	Citeseer	PubMed	Computers	Photo	Chameleon	Squirrel	Texas	Cornell
Tensorflow	79.08	65.96	83.74	83.44	92.06	67.61	51.82	88.02	85.25
Paddle
Origin	79.51	67.63	85.07	82.90	91.93	67.48	49.93	92.92	91.36

GPRConv

In file layers/conv/gpr_conv.py

The basic structure is similar to gcn_conv.py

Provides the GPR-GNN conv layer
In __init__ method: Add the initialization for learnt weights $\gamma_k$ and define the steps of propagation $K$
- For initializaiton method provided:
  - $\textbf{SGC}$: $\gamma_k = \delta_{kK}$, weight for the last layer is set to $1$, others as $0$;
  - $\textbf{PPR}$: $\gamma_k=\alpha(1-\alpha)^{k}\text{ for } k<K \text{ and }\gamma_K=(1-\alpha)^K$;
  - $\textbf{NPPR}$: $\gamma_k=\alpha^{k} / \sum\limits_{k=0}^{K}\alpha^{k}$;
  - $\textbf{Random}$: $\gamma\sim 2\sqrt{\frac{K+1}{3}}\mathbf{U}(-\sqrt{\frac{3}{K+1}},\sqrt{\frac{3}{K+1}})$
  - $\textbf{WS}$: User defined initial value for $\gamma_k$
- For $K$: recommended value is 10 (proposed by GPR-GNN origin paper)
In reset_parameters method: Use $\textbf{PPR}$ initializaiton method to reset parameters

GPRGNNModel

In file models/gprgnn.py

Provides the complete GPR-GNN model
In __init__ method: besides basic dimension information of node features and num of classes, GPR related hyperparameters ($K$, $\textbf{Init}$, $\alpha$, $\text{dprate}$, etc.) are also transfered to the network
In forward propagation:
- After feature extraction, it passes a special dropout layer controled by $\text{dprate}$ before entering propagation layers.
- Then, those representations of nodes are transferred into GPRConv

NormalizeFeatures

In file transforms/normalize_features.py

In file examples/gprgnn/gprgnn_trainer.py

Function random_planetoid_splits is defined to randomly split dataset and ensure the numbers of nodes from each classes are the same in training set.
Maximum of epoches is set as 1000 and early stopping trick is applied.
Early stopping mechanism is used.

In file examples/gprgnn/Reproduce_GPRGNN.sh

To keep the same with experiments in origin paper,5 homophily graph datasets, Cora, Citeseer, PubMed, Computers and Photo , adopt sparse split $(2.5%/2.5%/95%)$ for (training/validation/test), and 4 heterophily graph datasets, Chameleon, Squirrel, Texas and Cornell, adopt dense split $(60%/20%/20%)$ .
Train model with each hyperparameter setting for 10 times and calculate the average accuracy.
Best hyperparameters for different datasets are given in the file.

In file examples/gprgnn/test.accuracy

	Cora	Citeseer	PubMed	Computers	Photo	Chameleon	Squirrel	Texas	Cornell
Tensorflow	79.08	65.96	83.74	83.44	92.06	67.61	51.82	88.02	85.25
Paddle
Origin	79.51	67.63	85.07	82.90	91.93	67.48	49.93	92.92	91.36