Graph Neural Networks Final Year Projects with Source Code

Byadmin Final Year Projects

Graph Neural Networks Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Graph Neural Networks projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Graph Neural Networks Final Year Projects

  1. A Novel Rumor Detection Method Based on Non-Consecutive Semantic Features and Comment Stance
    This project focuses on detecting rumors on social media more accurately and quickly. It looks at both the hidden meaning in posts and the opinions of users who comment. The system gives more importance to trustworthy users and learns patterns over time. Tests show it can detect rumors faster and better than existing methods.
  2. Classification of Diabetic Retinopathy Disease Levels by Extracting Topological Features Using Graph Neural Networks
    This project focuses on improving the detection of diabetic retinopathy, a major cause of blindness, from retinal images. It uses a new deep learning approach that combines feature extraction and graph-based analysis to better capture important details in the images. The model was tested on public datasets and showed higher accuracy and reliability than existing methods. It helps doctors by making disease diagnosis faster and more precise.
  3. APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System
    This project focuses on detecting advanced cyberattacks in industrial systems connected through the Internet of Things. It uses a special machine learning method called Graph Attention Networks to identify hidden attacks more accurately than traditional methods. The approach was tested on real datasets and achieved over 95% detection accuracy in just around 20 seconds. Overall, it improves cybersecurity in smart industrial systems.
  4. IoT Network Cybersecurity Assessment With the Associated Random Neural Network
    This project develops a smart system to detect hacked devices in an IoT network. It uses a special type of neural network that looks at all devices together instead of checking them one by one. The system learns from real attack data to decide whether each device is safe or compromised. Tests show it works better than older methods.
  5. An Adaptive Masked Attention Mechanism to Act on the Local Text in a Global Context for Aspect-Based Sentiment Analysis
    This project studies how to understand opinions about specific parts of a sentence, such as features of a product. It introduces a new way for the model to focus on both the whole sentence and the important local words. This method reduces noise and helps the system learn useful information more efficiently. The model works well on many benchmark datasets.
  6. An Enhanced Recommendation Model Based on Review Text Graph and Interaction Graph
    This project improves how online recommendation systems understand users. It uses both the text of user reviews and user ratings to learn what people like. The model studies the full structure of review sentences, not just nearby words. It then combines this with rating patterns to give more accurate recommendations.
  7. Evolution of Deep Learning-Based Sequential Recommender Systems From Current Trends to New Perspectives
    This project studies how modern recommendation systems work. It focuses on systems that learn users’ preferences over time to give better suggestions. The study explains how models like RNNs, CNNs, GANs, GNNs, and transformers are used to understand user behavior. It also looks at methods that handle sparse data to improve recommendations.
  8. GNNGLY Graph Neural Networks for Glycan Classification
    This project focuses on studying glycans, which are complex sugar molecules important for many biological processes and diseases. The researchers created a model called GNNGLY that treats glycans like graphs to better understand their structure. The model can classify glycans into different categories and predict their immune-related properties. It performs better than traditional methods and existing tools, helping scientists study glycans more effectively.
  9. MixNet Physics Constrained Deep Neural Motion Prediction for Autonomous Racing
    This project focuses on predicting how other racecars will move around an autonomous racecar. It combines deep learning with physics rules to make predictions both accurate and safe. The method improves over traditional models by keeping predictions realistic and avoiding errors like going off-track. It was tested in simulations and used on a real autonomous racecar in a competition.
  10. Optimal Recommendation Models Based on Knowledge Representation Learning and Graph Attention Networks
    This project improves recommendation systems using knowledge graphs. It creates a new model, Cluster TransD, that efficiently represents items and their relationships. Another model, Cluster TransD-GAT, considers how users value different item connections. Experiments show these models give more accurate and relevant recommendations than existing methods.
  11. Resolving Power Equipment Data Inconsistency via Heterogeneous Network Alignment
    This project focuses on fixing inconsistent data about power equipment from different sources in China’s state grid. It introduces HENGE, a model that links and aligns device entries that have different information across sources. HENGE creates a network showing how devices are related and uses advanced learning techniques to improve accuracy. The model can work well even with limited labeled data and was tested successfully on real-world datasets.
  12. SoLGR Social Enhancement Group Recommendation via Light Graph Convolution Networks
    This project focuses on improving group recommendations on social networks. It studies how to represent users, items, and groups using their interaction and social connection data. The researchers designed a new model called SoLGR, which uses graph-based methods to combine these representations. The method helps make better predictions for what groups and individual users might like, and it works well on real-world datasets.
  13. Explainable Artificial Intelligence for Patient Safety A Review of Application in Pharmacovigilance
    This project looks at using explainable artificial intelligence (XAI) in monitoring the safety of medicines. It reviews studies that analyze clinical and drug data to detect side effects and drug interactions. The research highlights that while AI is widely used in drug safety, XAI is rarely applied. It also identifies challenges and future opportunities for making AI decisions more transparent in pharmacovigilance.
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