GammaGL/examples/gprgnn

• GPR-GNN
• • Structure
  • Results
  • Details
  • • • GPR-GNN
      • Convolutional layer class
      • Model class
      • Transforms class
      • Trainer
      • Experiment
      • Result

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.

Structure

Results

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

Details

GPR-GNN

Convolutional layer class

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

Model class

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

Transforms class

NormalizeFeatures

In file transforms/normalize_features.py

• Provides a row-wise normalization for feature matrix

Trainer

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.

Experiment

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.

Result

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

• Test accuracy on Texas and Cornell is unstable, fluctuating from 75 ~ 95.