Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT’s Potential to Apply Graph Layout Algorithms

Abstract

Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions—such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT. These algorithms are used to improve the readability of graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs’ ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs’ capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs; a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing while providing a balanced assessment of the challenges and opportunities they present.

Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models

“…Often, in traditional visualization pipelines, a designer has to either be proficient in a programming language, or translate their ideas into tool‐specific operations [ SSL * 22 ], which makes for a steep learning curve. A number of tools seek to simplify the process, using either visual interfaces [ MC21 ], or, more recently, natural language [ SLJL10 , MS23 , DBSSD23 , WCA23 ] interfaces, which allow users to produce visualizations by simply typing or speaking their questions or requests. Recent surveys [ WCWQ22 , WWS * 22 , WH22 ] have explored how machine learning is being applied to the data visualization process.…”