packages

gnn-exp: Visual Exploration and Explanation of Graph Neural Networks in Your Computational Notebooks

Advisor: Dr. Qianwen Wang

Institution: the Department of CompSci & Eng, UMNTC

Duration: November 2025 - Present

Link: [Codes] [Demo] [PyPl] [Paper] 

Abstract

gnn-exp is a Python package that integrated a diverse set for visualizing graph neural networks. The package has a graph visualizer, a graph editor, and a graph neural network visualizer. The package can perform visualizations and interactions such as hierachical structures and dual views to support better sensemaking and explainability to technical users. gnn-exp was adopted from our previous work, GNN101. 

Outcome

Y. Lu and Q. Wang (2025). gnn-exp: Visual Exploration and Explanation of Graph Neural Networks in Your Computational Notebooks [Computer Software]. Python Package Index. https://test.pypi.org/project/gnn-explorer/ 

LargeGraphs.jl: Sensemaking of Large Graphs in Julia

Independent Research Project

Institution: the Department of CompSci & Eng, UMNTC

Duration: March 2026 - Present

Link: [Codes] [Demo] [JuliaHub] [Paper] 

Abstract

LargeGraphs is a lightweight Julia package for interactive graph visualization in IJulia and Jupyter notebooks. It accepts simple Julia data structures for nodes and edges, avoiding the need for users to adopt a specialized graph type before rendering. The package provides notebook-friendly HTML output based on Sigma.js, supports standalone HTML export for sharing, and includes several built-in layout options such as random, circular, grid, and force-directed layouts. It also offers practical interaction features for large-graph exploration, including fit-to-view controls, camera locking, and notebook-oriented resource management to prevent rendering failures when many visualizations are displayed in a single session. LargeGraphs is designed for fast visual inspection, teaching, prototyping, and exploratory analysis, with an emphasis on simplicity, portability, and ease of integration into Julia-based notebook workflows. LargeGraphs.jl outperforms standard graph visualization library GraphMakie.jl in terms of rendering and exporting performance. 

Outcome

Y. Lu. (2026). LargeGraphs.jl: Sensemaking of Large Graphs in Julia

 [Computer Software] Github Repository. https://github.com/HarryLuUMN/LargeGraphs.jl