https://github.com/tsungtsetu122/patternrecognition-gnn-node-classification

This project implements a Mixed Graph Neural Network (GNN) for semi-supervised multi-class node classification using the Facebook Large Page-Page Network Dataset. The goal of the project is to predict the class of each node (page) based on 128-dimensional feature vectors and the graph structure connecting the nodes.
https://github.com/tsungtsetu122/patternrecognition-gnn-node-classification

evaluation-metrics gnn matplotlib networkx numpy optimization pandas python pytorch-geometric

Last synced: 7 months ago
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This project implements a Mixed Graph Neural Network (GNN) for semi-supervised multi-class node classification using the Facebook Large Page-Page Network Dataset. The goal of the project is to predict the class of each node (page) based on 128-dimensional feature vectors and the graph structure connecting the nodes.

• Host: GitHub
• URL: https://github.com/tsungtsetu122/patternrecognition-gnn-node-classification
• Owner: TsungTseTu122
• License: apache-2.0
• Created: 2025-02-19T09:02:36.000Z (8 months ago)
• Default Branch: topic-recognition
• Last Pushed: 2025-02-27T03:45:46.000Z (7 months ago)
• Last Synced: 2025-03-10T05:54:24.619Z (7 months ago)
• Topics: evaluation-metrics, gnn, matplotlib, networkx, numpy, optimization, pandas, python, pytorch-geometric
• Language: Python
• Homepage:
• Size: 6.41 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Pattern Analysis

Pattern Analysis of various datasets by COMP3710 students in 2024 at the University of Queensland.

We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX.

This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students.

The library includes the following implemented in Tensorflow:

* fractals 

* recognition problems

In the recognition folder, you will find many recognition problems solved including:

* segmentation

* classification

* graph neural networks

* StyleGAN

* Stable diffusion

* transformers

etc.