Graph Neural Network Projects

Start with these graph neural network projects

• Node Classification Implementation ProjectBest first GNN task; easy to explain with Cora or CiteSeer and clear accuracy/F1 reporting.
• Link Prediction in Graphs ProjectGood for ROC-AUC and average precision practice on graph links and missing-edge prediction.
• Graph Attention Networks ProjectUseful for comparing attention heads, aggregation and interpretability on citation graphs.
• GraphSAGE Node Embeddings ProjectStrong baseline for sampling and mini-batch training on larger graphs such as Reddit or ogbn-arxiv.
• Temporal Graph Neural Networks ProjectGood if you want time-aware graphs, event streams and a more advanced final year report.
• Heterogeneous Graph Neural Networks ProjectUseful for real-world multi-type nodes and edges, such as papers, authors and venues.
• Recommendation with GNNs ProjectPractical recommendation project with top-k metrics and clear evaluation for ranked outputs.
• Explainable GNN Models ProjectGood for a deeper report because it covers saliency, node importance and explainability methods.

How to choose graph neural network projects by level

Start with a problem that matches your comfort with graphs. Node classification, link prediction and GraphSAGE are easier because public datasets and reference baselines are abundant. Graph attention networks and graph convolutional networks are still manageable for a final year report. If you want more depth, try heterogeneous graphs, temporal GNNs or explainable GNN models, where the novelty comes from modelling choices, not just training a standard layer.

What you need for graph neural network projects: Python, PyTorch and data

Most projects can be built with Python, NumPy, NetworkX and either PyTorch Geometric or DGL. Useful datasets include Cora, CiteSeer, PubMed, ogbn-arxiv, Reddit, MUTAG, IMDB-BINARY and OGB link prediction splits. Learn sparse matrices, message passing, neighbourhood sampling and regularisation such as dropout and weight decay. If your idea uses graphs from sensors, social media or molecules, document how you converted raw records into nodes, edges and features.

How examiners evaluate graph neural network projects

Examiners usually look for problem clarity, correct graph construction, baseline comparison and honest evaluation. A good report compares your GNN with a simple MLP, logistic regression, random walk features or a non-graph baseline. Show ablation results for hidden size, number of layers, aggregation type and edge features. For final year assessment, include confusion matrix, ROC-AUC, precision-recall curves or top-k metrics, depending on the task.

Common mistakes in graph neural network projects

The most common issue is copying a starter notebook that only trains one layer and calling it a complete project. Another pitfall is leaking labels through transductive splits, especially on citation graphs. Students also ignore class imbalance, disconnected components, noisy edges and the cost of full-batch training on large graphs. Write down why one model fails, when sampling is needed, and how you would improve results with better features or graph augmentation.

All 40 graph neural network project ideas

1. 0401Node Classification Implementation ProjectApplied
2. 0402Link Prediction in Graphs ProjectApplied
3. 0403Graph Clustering Implementation ProjectApplied
4. 0404Community Detection in Graphs ProjectApplied
5. 0405Graph Convolutional Networks ProjectApplied
6. 0406Graph Attention Networks ProjectApplied
7. 0407GraphSAGE Node Embeddings ProjectApplied
8. 0408Graph Autoencoders ProjectApplied
9. 0409Graph Generative Models ProjectApplied
10. 0410Temporal Graph Neural Networks ProjectApplied
11. 0411Heterogeneous Graph Neural Networks ProjectApplied
12. 0412Knowledge Graph Embeddings ProjectApplied
13. 0413Graph Representation Learning ProjectApplied
14. 0414Graph Pooling Methods ProjectApplied
15. 0415Graph Isomorphism Networks ProjectApplied
16. 0416Message Passing Neural Networks ProjectApplied
17. 0417Graph Diffusion Models ProjectApplied
18. 0418Graph Transformers Implementation ProjectApplied
19. 0419Graph Few-Shot Learning ProjectApplied
20. 0420Graph Reinforcement Learning ProjectApplied
21. 0421Social Network Analysis Tools ProjectApplied
22. 0422Molecular Graph Prediction ProjectApplied
23. 0423Citation Network Analysis ProjectApplied
24. 0424Traffic Network Analysis ProjectApplied
25. 0425Recommendation with GNNs ProjectApplied
26. 0426Fraud Detection with GNNs ProjectApplied
27. 0427Anomaly Detection in Graphs ProjectApplied
28. 0428Graph Neural Networks for NLP ProjectApplied
29. 0429Graph Neural Networks for Vision ProjectApplied
30. 0430GNNs for Combinatorial Optimisation ProjectApplied
31. 0431Physics Simulations with GNNs ProjectApplied
32. 0432Drug Discovery with GNNs ProjectApplied
33. 0433Protein Structure Prediction with GNNs ProjectApplied
34. 0434GNNs for Social Good ProjectApplied
35. 0435Explainable GNN Models ProjectApplied
36. 0436Graph Adversarial Attacks ProjectApplied
37. 0437Graph Adversarial Defenses ProjectApplied
38. 0438Graph Contrastive Learning ProjectApplied
39. 0439Graph Self-Supervised Learning ProjectApplied
40. 0440Graph Attention Visualisation ProjectApplied

Graph Neural Network Projects: questions students ask

Which graph neural network project is best for a beginner?

Node classification with Cora or CiteSeer is the easiest starting point. It has standard splits, clear metrics and many baselines, so you can focus on understanding message passing.

Do I need advanced maths for these projects?

Not much beyond linear algebra, probability and backpropagation. For most final year work, the key is understanding adjacency, embeddings and loss functions.

Which datasets are best for graph neural network projects?

Cora, PubMed, ogbn-arxiv, MUTAG and OGB link prediction splits are common. Pick a dataset that matches your task and can be explained clearly in your report.

How do I show novelty in a GNN project?

Add a new feature set, sampling strategy, explainability layer, or evaluation on a different graph type. Novelty can be in the pipeline, not only the model.

Why do the starters feel small?

These pages review honest Python starters, and many are only baselines. You are expected to extend them with preprocessing, training, evaluation and discussion.