Representing Long-Range Context for Graph Neural Networks with Global Attention

Last update: Dec 30, 2022

Overview

Graph Augmentation

Graph augmentation/self-supervision/etc.

Algorithms

gcn
gcn+virtual node
gin
gin+virtual node
PNA
GraphTrans

Augmentation methods

None
FLAG
Augmentation

Installation

To setup the Python environment, please install conda first.

All the required environments are in setup.sh.

How to Run

To run experiments:

CUDA_VISIBLE_DEVICES=0 python main.py \
    --configs configs/code2/gcn-virtual/baseline+run1+seed.yml

# Or to use slurm
sbatch ./slurm-run.sh configs/code2/gcn-virtual/baseline+run1+seed.yml

Exps

GNN-Transformer

CUDA_VISIBLE_DEVICES=0 python main.py \
    --configs configs/code2/gnn-transformer/JK=cat/pooling=cls+norm_input.yml

Owner

UC Berkeley RISE

REAL-TIME INTELLIGENT SECURE EXPLAINABLE SYSTEMS

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Representing Long-Range Context for Graph Neural Networks with Global Attention

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