{"id":23157,"date":"2025-01-20T09:31:17","date_gmt":"2025-01-20T09:31:17","guid":{"rendered":"https:\/\/socialmedialab.ca\/web\/?p=23157"},"modified":"2025-02-06T21:31:37","modified_gmt":"2025-02-06T21:31:37","slug":"graphoptima-graph-layout-optimization-open-access-framework-visualizing-large-networks","status":"publish","type":"post","link":"https:\/\/socialmedialab.ca\/web\/2025\/01\/20\/graphoptima-graph-layout-optimization-open-access-framework-visualizing-large-networks\/","title":{"rendered":"[New Paper + Source Code] GraphOptima: A graph layout optimization framework for visualizing large networks"},"content":{"rendered":"\n

We are excited to announce the publication of a new paper titled \u201c<\/em>GraphOptima: A graph layout optimization framework for visualizing large networks<\/strong><\/em><\/a>\u201d in Software X<\/em> by Anatoliy Gruzd (Toronto Metropolitan University), Jingwei Zhang (University of Toronto), and Philip Mai (Toronto Metropolitan University).<\/p>\n\n\n\n

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Graphs are crucial in visualizing online and offline networks, their participants and interactions. These visual representations have become indispensable tools for studying various issues, including the propagation of misinformation, botnet activities, algorithmic filtering, and the spread of disease. Researchers rely on these graphs to uncover underlying structures, formulate hypotheses, and communicate their findings to the public.<\/p>\n\n\n\n

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However, creating visually appealing and informative network visualizations is no simple task, especially for large networks with millions of nodes and edges. It often requires researchers to manually experiment with multiple layout algorithms and fine-tune parameters to achieve the desired result. This process is time-consuming, particularly for large networks, where the computational complexity of rendering a graph can lead to significant delays between adjustments and results. These challenges can hinder iterative exploration and analysis.<\/p>\n<\/div><\/div>\n\n\n\n

To address these issues, this paper introduces GraphOptima<\/strong><\/a>, an open-source framework designed to optimize graph layouts and improve readability metrics. GraphOptima automates key steps in the visualization process, including parameter selection<\/strong>, layout computation<\/strong>, and readability metric evaluation<\/strong>. Unlike conventional approaches that aim to provide a single \u201coptimal\u201d solution, GraphOptima generates a set of solutions by applying multi-objective optimization<\/strong>. This allows researchers to compare and evaluate different layouts based on multiple readability metrics, such as:<\/p>\n\n\n\n