Graph Neural Networks projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Graph Neural Networks projects are designed for final year project submissions, research work, and publishing research papers. These projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Graph Neural Networks Projects
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A Comprehensive Review on Leveraging Machine Learning for Multi-Agent Path Finding
The project aims to explore how Machine Learning can improve Multi-Agent Path Finding. It focuses on enabling multiple agents to move from their starting points to goals without collisions. The research examines how ML enhances the efficiency and coordination of agents in complex environments. It studies environment representation, path planning, and execution of solutions. The goal is to understand and highlight how ML can transform multi-agent navigation in large-scale automated systems like warehouses. -
Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques
This project focuses on improving how data moves across a network. It uses machine learning to predict traffic patterns for several time periods ahead. The method helps route data more efficiently, reducing sudden network changes. It also lowers the cost of monitoring the network while keeping performance close to the best possible. -
Capacitated Shortest Path Tour-Based Service Chaining Adaptive to Changes of Service Demand and Network Topology
This project focuses on making computer networks smarter and more efficient. It finds the best path for network services to travel through virtual functions while considering network limits. The system learns from past data using advanced AI techniques to quickly adapt to changes. Tests show it works almost as well as exact solutions but much faster. -
Learning End-to-End Hybrid Precoding for Multi-User mmWave Mobile System With GNNs
This project focuses on improving wireless communication in millimeter wave systems. It uses a smart learning method to design the transmitter signals directly from received signals. This approach works well even when users move or the network changes. The method reduces errors, speeds up communication, and works for different network setups without extra training. -
A Graph-Based Multi-Scale Approach With Knowledge Distillation for WSI Classification
This project is about teaching computers to analyze very large medical images for disease detection. Normally, labeling these images takes too much time. The researchers created a new method that looks at the images at different zoom levels and learns how parts of the image relate to each other. Their approach makes predictions more accurate and works better than previous methods on standard datasets. -
CiGNN A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation
This project develops a new method to measure blood pressure without using a cuff. It finds which body signals actually affect blood pressure and then uses a smart computer model to track changes beat by beat. The method works accurately for people of different ages and health conditions. It can help monitor blood pressure continuously and more reliably than current approaches. -
DeepSIG A Hybrid Heterogeneous Deep Learning Framework for Radio Signal Classification
This project develops a smart system called DeepSIG to identify types of radio signals. It combines three different AI models to look at signals in multiple ways, like sequences, images, and graphs. The system learns from all these views together to make more accurate predictions. Tests show it works better than using any single method, especially when only a few signals are available.
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How We Help You with Graph Neural Networks Projects
At UniPhD, we provide complete guidance and support for Graph Neural Networks projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.
Our experts have extensive experience guiding students in computer science, electronics, and electrical domains, ensuring successful completion of academic and research projects.
Graph Neural Networks Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Graph Neural Networks research and thesis development. We offer fast-track dissertation writing services to help you complete your Graph Neural Networks thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Graph Neural Networks Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Graph Neural Networks final year project.
Graph Neural Networks Research Support for PhD Scholars
UniPhD offers advanced Graph Neural Networks research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.