Graph Convolutional Network Final Year Projects with Source Code

Byadmin Final Year Projects

Graph Convolutional Network Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Graph Convolutional Network 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 Convolutional Network Final Year Projects

  1. A Hybrid Deep Learning-Based Intelligent System for Sports Action Recognition via Visual Knowledge Discovery
    This project focuses on creating an intelligent system to recognize sports actions, especially in aerobics. It uses video frames to analyze human movements and understand actions. The system represents the human body as a skeleton and studies its motion over time. Experiments show that it can accurately identify actions better than existing methods.
  2. 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.
  3. Comparison of Real-Time and Batch Job Recommendations
    This project focuses on improving recommendation systems that suggest items like jobs to users. It compares traditional batch-based methods with a real-time approach that considers users’ latest actions. Using a graph-based model, the real-time system gave better results, leading to more user engagement. The study shows how real-time recommendations can be more effective in practical applications.
  4. Deep Learning Using Context Vectors to Identify Implicit Aspects
    This project focuses on finding the hidden topics that people talk about in their reviews. It looks for meanings that are not directly written but are implied through the words people use. The system learns from examples and understands the surrounding text to detect these hidden ideas. It helps improve sentiment analysis by making it more accurate and closer to real human understanding.
  5. 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.
  6. 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.
  7. 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.
  8. A Cross-Lingual Hybrid Neural Network With Interaction Enhancement for Grading Short-Answer Texts
    This project focuses on automatically grading short student answers using AI. It combines deep learning techniques to better understand the meaning of students’ responses. The system compares student answers with reference answers and enhances their interaction to improve scoring accuracy. Experiments show it works well for both Chinese and English answers.
  9. STGL-GCN SpatialTemporal Mixing of Global and Local Self-Attention Graph Convolutional Networks for Human Action Recognition
    This project focuses on recognizing human actions using skeleton data from videos. The method looks at both local and global connections between body joints to better understand movements. It uses a special neural network that learns which joint connections are most important for each action. Tests show it can accurately identify different human actions.
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