PASTEL

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Code for "Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing"

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# PASTEL
Code for "Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing" (accepted by CIKM 2022).

## Overview
- main.py: getting started.
- src/model.py: model configurations.
- src/model_handler.py: overall framework of PASTEL. 
- src/models/graph_clf.py: configurations for GNN backbones and the graph structure learners.
- src/layers/backbones.py: the basic GNNs we used as the backbone, including GCN, GAT, APPNP and GraphSAGE.
- src/layers/graph_learner.py: graph learner of the input graph.
- src/utils/*.py: utils for data preprocessing, loading and other functions.

## Requirements
The implementation of PASTEL is tested under Python 3.8.12, with the following packages installed:
* `dgl==0.8.0.post1`
* `dgl_cu111==0.7.2`
* `networkx==2.6.3`
* `numpy==1.20.3`
* `PyYAML==6.0`
* `scikit_learn==1.1.1`
* `scipy==1.7.1`
* `torch==1.9.1+cu111`
* `torch_geometric==2.0.1`

You can install the above packages by pip:
* `pip install -r requirements.txt`

Or by conda:
* `conda install --yes --file requirements.txt`

## Datasets
All the datasets (i.e., Cora, Citeseer, Photo, Actor) are provided by [pytorch_geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets).

## Run the codes
Train and evaluate the model:
* `python main.py -config config/<dataset>.yml`

We train the PASTEL with GNN backbones, and report the `training loss`, `validation loss`, `training accuracy`, `validation accuracy` and the `test accuracy`.
For instance:
* `python main.py -config config/cora.yml`

PASTEL

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