-
Notifications
You must be signed in to change notification settings - Fork 1
Home
CoreRec is your all-in-one recommendation engine for graph-based algorithms. Seamlessly integrating advanced neural network architectures, CoreRec excels in node recommendations, model training, and graph visualizations, making it the ultimate tool for data scientists and researchers. VishGraphs is your ultimate Python library for graph visualization and analysis. Whether you're a data scientist, researcher, or hobbyist, VishGraphs offers intuitive tools to generate, visualize, and analyze graphs effortlessly.
This document provides a summary of the models used to benchmark the CoreRec model. The models are categorized into different types of graph-based algorithms.
| Category | Model Name | Description |
|---|---|---|
| Graph Neural Networks (GNNs) | Graph Convolutional Networks (GCNs) | Extend convolutional networks to graph data, capturing local neighborhood information. |
| Graph Attention Networks (GATs) | Use attention mechanisms to weigh the importance of neighboring nodes differently. | |
| GraphSAGE | Generates node embeddings in an inductive manner, handling new, unseen nodes during training. | |
| ... | ||
| graphtransformer | dngscore | The model being benchmarked against the above models. |
To benchmark the CoreRec model against the above models, follow the steps in the provided Jupyter notebook.
The results of the benchmarking will be visualized in a bar chart, comparing various metrics such as precision, recall, F1 score, accuracy, specificity, and sensitivity across all models.
generate_graph