SpreadLine

A visualization design framework that supports the exploration of dynamic multivariate networks from the egocentric perspective. This repo is part of the research paper "SpreadLine: Visualizing Egocentric Dynamic Influence" (link), published in IEEE Transactions on Visualization and Computer Graphics.

Introduction

• Source code can be found in ./SpreadLine
• Sample script of using SpreadLine refers to ./sample.py
• supplementary_materials.pdf contains 5 major sections: (A) Examples of different optimization focuses, optimization details, and two more discussions on design choices; (B) Interface of using SpreadLine; (C) Data structures of all three case studies and their associated ChatGPT prompts for data processing (if there is any); (D) Self-reported participant background & additional usability study details; (E) Two more SpreadLine representations of visualization researchers on a larger data scale.
• ./case-studies provide the datasets used in SpreadLine representations.
• ./demo contains a vanilla web application that computes and renders SpreadLine representations.

Python Installation

Run this to use SpreadLine as demonstrated in ./sample.py

pip install .

Roadmap

• Code documentations and cleanup.

How to Cite

If you use any parts of this repo, please cite:

Kuo, Y.-H., Liu, D., & Ma, K.-L. (2024). SpreadLine: Visualizing egocentric dynamic influence. IEEE Transactions on Visualization and Computer Graphics.

or in bibtex:

@article{kuo2024spreadline,
  title={SpreadLine: Visualizing egocentric dynamic influence},
  author={Kuo, Yun-Hsin and Liu, Dongyu and Ma, Kwan-Liu},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2024},
  publisher={IEEE}
}