TODO.md

GNN Tracking

My solutions and submission to the qualification task of the GSoC 2023 “GNN Tracking” project under CERN, IRIS-HEP

TODO for Machine learning & statistics

Deliverable: A Jupyter Notebook completing the task below.

Task: Edge classification with pytorch geometric

Train an Edge Classifier Graph Neural Network to classify the edges (given by the edge_index) as true or false (given by the array y).

Inputs for training/inference :

• x (the node features)
• edge_features.

Basic Plan

• Use binary cross-entropy as loss function for the classification.

• Look into other loss functions

• First use basic accuracy to evaluate the model

• Modify the model evalutation technique to acknowledge the class imbalance

Note:

• The basic structure of the code -> similar to last section of pytorch geometric tutorial.

Bonuses:

• Extract relevant reusable code (models etc.) to a python library/python files.

• Include the model in the gnn_tracking/models/ module of the forked gnn_tracking repository (see instructions for the software engineering tasks) and find good locations for other reusable components that you have created.

If time allows

• Use GraphGym to illustrate experiments and performance of different architecturesand losses

• Use LinkLoader for negative sampling

• VGAE