Graph Neural Network Research

About Course

Course Duration: 4 Months

Collaborate with us on Graph Neural Networks (GNNs) Research

This program is designed for research experts, passionate student researchers, and industry professionals who wish to work on GNN research projects. We focus on transforming theoretical concepts into practical innovations.

The work undertaken in this program is intended to set new standards in the field of GNN research.

Course Content

Welcome to the GNN research program!
Orientation and onboarding.

Welcome to the program.
First 1-1 call with your mentor.
Research interest summary

Choosing the research problem
Introduction to GNN research areas. Learn about common research domains such as social networks, molecules, material science, recommendation systems, and temporal graphs.

Data preprocessing and setup

Developing the GNN model
Now, at this stage, you have finalised your research topic, a sufficient literature review has been made, and the dataset is clean, preprocessed, and ready to use. Now it is time to build the GNN model and train it on our dataset.

Advancing the model
Now you already have a baseline model. It is the time to extend beyond basics, by experimenting with GraphSAGE, TGN, or spatio-temporal GNNs depending on your problem.

Writing the research paper

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What Will You Learn?

Course Content

Welcome to the GNN research program! Orientation and onboarding.

Welcome to the program.

First 1-1 call with your mentor.

Research interest summary

Choosing the research problem Introduction to GNN research areas. Learn about common research domains such as social networks, molecules, material science, recommendation systems, and temporal graphs.

Social networks

GNNs for molecules and material science

Recommendation systems and temporal graphs

Conducting literature review

Finalising your research problem

Paper summaries and proposal

Identifyng gaps and open problems

Upload the relevant research papers for reference

Data preprocessing and setup

Understanding graph datasets

Data selection

Data preprocessing

Data preprocessing

Developing the GNN model Now, at this stage, you have finalised your research topic, a sufficient literature review has been made, and the dataset is clean, preprocessed, and ready to use. Now it is time to build the GNN model and train it on our dataset.

Overview of GNN architectures.

Designing experiments

Initial baseline models

Baseline report

Model selection for your research problem

Training and evaluation

First model training

Advancing the model Now you already have a baseline model. It is the time to extend beyond basics, by experimenting with GraphSAGE, TGN, or spatio-temporal GNNs depending on your problem.

Modifying or developing new variants

Comparative study and mentor discussion

Comparative results report

Writing the research paper

Research paper structure

Best writing practices

zeroth draft submission

Submission of first draft

Submission of final draft.

Student Ratings & Reviews

Welcome to the GNN research program!
Orientation and onboarding.

Choosing the research problem
Introduction to GNN research areas. Learn about common research domains such as social networks, molecules, material science, recommendation systems, and temporal graphs.

Developing the GNN model
Now, at this stage, you have finalised your research topic, a sufficient literature review has been made, and the dataset is clean, preprocessed, and ready to use. Now it is time to build the GNN model and train it on our dataset.

Advancing the model
Now you already have a baseline model. It is the time to extend beyond basics, by experimenting with GraphSAGE, TGN, or spatio-temporal GNNs depending on your problem.