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Graph Neural Network Projects.

50+ curated GNN project topics for BE, BTech and MTech — node classification, link prediction, molecular property prediction, knowledge graph embedding, spatiotemporal forecasting, graph recommenders and explainable GNN with PyTorch Geometric, DGL and Spektral. Complete code, report, PPT and viva support.

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Node Classification Link Prediction Molecular Graphs Knowledge Graphs Spatiotemporal GNN Recommenders Heterogeneous GNN Explainable GNN

Graph Neural Network Projects for Final Year Students (2026)

Graph Neural Networks (GNNs) learn representations on graph-structured data — social networks, molecules, knowledge bases, traffic networks and recommendation graphs. They power node classification, link prediction, graph classification and spatiotemporal forecasting.

This page lists 50+ high-impact GNN project topics aligned with current research (NeurIPS, ICML, ICLR, KDD). Frameworks include PyTorch Geometric (PyG), Deep Graph Library (DGL), Spektral, NetworkX and OGB benchmarks. Ideal for CSE, AI/ML, Data Science and research students in Bangalore and across India.

Core Frameworks & Tools

Libraries and platforms commonly used in academic and industrial GNN projects.

PyTorch Geometric DGL Spektral NetworkX PyTorch OGB Benchmarks

Best Graph Neural Network Project Topics & Tools

Grouped by research theme. Each topic lists primary frameworks and supporting libraries.

# Project Topic Primary Tools / Frameworks
🔵  Node Classification & Semi-Supervised Learning on Graphs
1NodeSemi-Supervised Node Classification on Citation Networks (Cora / CiteSeer / PubMed)PyG, GCN / GAT / GraphSAGE, scikit-learn
2NodeGraph Attention Network (GAT) for Multi-Label Node ClassificationPyG / DGL, GATConv, OGB
3NodeGraphSAGE Inductive Node Embedding for Large GraphsPyG, GraphSAGE, NeighborSampler
4NodeOver-Smoothing Analysis and Residual / Jumping Knowledge GNNPyG, JKNet, residual GCN variants
5NodeFew-Shot Node Classification with Meta-Learning on GraphsPyG, MAML-style loops, episodic sampling
🟠  Link Prediction & Graph Completion
6LinkLink Prediction on Social / Citation Graphs with GAE / VGAEPyG, Graph Autoencoder, ROC-AUC
7LinkKnowledge Graph Link Prediction with R-GCN / CompGCNDGL-KE / PyG, FB15k-237, WN18RR
8LinkTemporal Link Prediction on Dynamic GraphsTorch Geometric Temporal, DyRep-style models
9LinkHeterogeneous Link Prediction in Multi-Relational NetworksPyG HeteroConv, HAN, MAGNN
10LinkNegative Sampling Strategies for Scalable Link PredictionPyG, custom samplers, OGB-Link
🟢  Molecular Graphs · Drug Discovery · Chemistry
11MolMolecular Property Prediction with GIN / GAT on MoleculeNetPyG, RDKit, MoleculeNet, GIN
12MolDrug–Target Interaction Prediction using Bipartite GNNPyG, DGL, binding affinity datasets
13MolGraph-based Toxicity and ADMET PredictionPyG, RDKit, Tox21 / ClinTox
14Mol3D Molecular Graphs with SchNet / DimeNet-style ModelsPyG, geometric message passing
15MolReaction Prediction and Retrosynthesis with Graph TransformersPyG, USPTO datasets, transformer GNN
🔵  Knowledge Graphs & Reasoning
16KGKnowledge Graph Embedding with TransE / RotatE + GNN RefinementDGL-KE, PyG, FB15k / WN18
17KGMulti-Hop Reasoning over Knowledge Graphs with GNNPyG, path-based models, Query2box-style
18KGEntity Alignment across Multi-lingual Knowledge GraphsPyG, GCN-Align, cross-lingual KG
19KGQuestion Answering over Knowledge Graphs with GNNPyG, GraftNet / PullNet-style, SPARQL
🟡  Spatiotemporal GNN · Traffic · Time Series on Graphs
20TempTraffic Flow Prediction with STGCN / Graph WaveNetPyG Temporal, METR-LA / PEMS datasets
21TempSpatiotemporal GNN for Weather / Air Quality ForecastingTorch Geometric Temporal, sensor grids
22TempDynamic Graph Neural Networks for Evolving NetworksDySAT / EvolveGCN, PyG Temporal
23TempEpidemic Spreading Prediction on Contact GraphsNetworkX, PyG, SIR-style simulations
🟣  Graph-based Recommendation Systems
24RecGraph Convolutional Matrix Completion for Collaborative FilteringPyG, GCMC, MovieLens
25RecLightGCN / NGCF for Implicit Feedback RecommendationPyG, RecBole, Amazon / Yelp datasets
26RecSession-based Recommendation with Temporal Graph NetworksPyG, SR-GNN, sequential sessions
27RecKnowledge-aware Recommendation with KG + User–Item GraphPyG, KGAT / KGCN, MIND / Amazon
🟢  Heterogeneous & Multiplex Graphs
28HetHeterogeneous Graph Attention Network (HAN) for Academic NetworksDGL / PyG, ACM / DBLP
29HetRelational GCN for Multi-Relational Node ClassificationPyG RGCNConv, AIFB / MUTAG
30HetMultiplex Network Embedding and ClassificationPyG, multi-layer graph construction
🔴  Explainable & Interpretable GNN
31XAIGNNExplainer for Subgraph-level Explanations of Node PredictionsPyG, GNNExplainer, visualization
32XAIPGExplainer and Model-level Explanations for Graph ClassificationPyG, PGExplainer, MUTAG / BBBP
33XAICounterfactual Explanations on GraphsCustom CF search, PyG, evaluation metrics
🏥  Domain Applications — Fraud · Security · NLP · Vision
34AppFraud Detection on Transaction / Payment GraphsPyG, GraphSAGE, imbalanced metrics
35AppCybersecurity: Malware / Intrusion Detection with Graph FeaturesNetworkX, PyG, system-call / network graphs
36AppText Classification with Graph of Words / Dependency GraphsPyG, TextGCN / BertGCN-style
37AppScene Graph Generation and Visual Relationship DetectionPyTorch, Faster R-CNN, GNN message passing
38AppProtein–Protein Interaction Prediction with GNNPyG, STRING / BioGRID, graph classification
39AppPower Grid / Smart Grid Stability Analysis with GNNPyG, IEEE bus systems, regression tasks
40AppSocial Influence / Cascade Prediction on Social GraphsNetworkX, PyG, diffusion models
🔬  Advanced & Research-Oriented GNN Topics
41AdvGraph Transformers vs Message-Passing GNNs — Comparative StudyPyG, Graphormer / SAN-style, OGB
42AdvSelf-Supervised Graph Representation Learning (GraphCL / BGRL)PyG, contrastive losses, linear evaluation
43AdvFederated Graph Neural Networks for Privacy-Preserving LearningPyG + Flower / FedML, partitioned graphs
44AdvScalable GNN Training with Neighbor Sampling and GraphSAINTPyG, GraphSAINT, Cluster-GCN
45AdvAdversarial Attacks and Defenses on Graph Neural NetworksPyG, Nettack / Metattack, robust GNN
46AdvGenerative Models for Graphs (GraphVAE / GraphRNN / DiGress)PyG, molecule generation, validity metrics
47AdvContinuous-Time Dynamic Graph LearningTGN / DyRep, Torch Geometric Temporal
48AdvHypergraph Neural Networks for Higher-Order RelationsCustom hypergraph conv, PyTorch
49AdvLogical Reasoning and Neuro-Symbolic Integration on GraphsPyG, rule injection, KG reasoning
50AdvEnd-to-End GNN Pipeline: Data → Training → Deployment DemoPyG, FastAPI / Streamlit, ONNX export

Topics reflect research themes at Stanford, MIT, Berkeley, CMU, ETH Zurich and venues such as NeurIPS, ICML, ICLR, KDD and WWW. Contact us for reference material, full Python source code, demo UI, evaluation metrics, university-format report, PPT and viva Q&A for any topic above.

Why Choose Us for GNN Projects?

Bangalore-based guidance for BE, BTech and MTech graph representation learning projects.

Node & Link Tasks

GCN, GAT, GraphSAGE, GAE/VGAE pipelines on citation, social and OGB benchmarks — full training, metrics and ablation studies.

Molecular & KG

MoleculeNet property prediction, drug–target graphs, knowledge graph embedding and multi-hop reasoning with RDKit and DGL-KE.

Spatiotemporal GNN

Traffic forecasting, dynamic graphs and sensor networks with STGCN, Graph WaveNet and Torch Geometric Temporal.

Explainability & Robustness

GNNExplainer, adversarial attacks/defenses and self-supervised graph learning matching current research standards.

Frequently Asked Questions — GNN Projects

Top topics include node classification on citation networks, link prediction with GAE/VGAE, molecular property prediction with GIN/GAT, knowledge graph embedding, traffic forecasting with STGCN, graph-based recommenders (LightGCN), heterogeneous GNNs and explainable GNN methods.
PyTorch Geometric (PyG), Deep Graph Library (DGL), Spektral, NetworkX, Torch Geometric Temporal, OGB benchmarks, RDKit (for molecules), Hugging Face, scikit-learn, PyTorch, FastAPI and Streamlit/Gradio for demos.
Yes. Topics reflect themes from Stanford, MIT, Berkeley, CMU, ETH Zurich and major venues (NeurIPS, ICML, ICLR, KDD, WWW) on graph representation learning, geometric deep learning and applications.
Yes. Packages include reference material, full Python source code, demo UI, evaluation metrics, university-format report (VTU/Anna/JNTU), PPT and viva Q&A.