Graph Machine Learning Project

This is project for the course: Introduction to Graph Machine Learning

Requirements

• pytorch
• pytorch geometric

Methodology

• Weights and biases

• teacher and student

  • student backbone make node invariant
    • student with gcn layers
    • student with FF layers

• results mean +- std

  • #seeds
  • #random subsample

• node classification techniques x 2

• student baseline with training data and then with training and knowledge distillation

• base model

     /------------- graph transformer\
  

  input = = concat \ ++++++++++++++ MP / slide 23, lecture 6

Tasks

Running the code

Slurm used

srun command

Or run the jupyter notebook cell by cell

Datasets

• Peptide func

Results

References

• https://arxiv.org/pdf/2403.03483
• https://arxiv.org/pdf/2302.00219
• https://openreview.net/forum?id=vzZ3pbNRvh
• https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9053986
• https://paperswithcode.com/sota/graph-classification-on-peptides-func

Members

• Bashar Jaan Khan
• Muneeb Khan
• Shameer