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GSOC 2026 PROJECT

Implement a Data-Driven Visual Attribute Mapping System in GraphSpace

Contributor
Akriti agrawal
Mentors
Not published
Organization
National Resource for Network Biology (NRNB)Science and medicine
Program
GSoC 2026

About this project

Description from the program archive

This project aims to implement a Visual Attribute Mapping System that enables users to automatically map node and edge attributes to visual properties within the GraphSpace interface. This system will allow users to generate meaningful graph visualizations where the appearance of graph elements directly reflects underlying attribute data — turning raw data into visual insight. There are two main components in this project: User Interface for Visual Attribute Mapping — A new Visual Attribute Mapping panel integrated into the GraphSpace Layout Editor. This interface will allow users to select graph attributes and map them to visual properties through an interactive configuration panel. Users will choose between Discrete Mapping (categorical attributes) and Continuous Mapping (numerical attributes). The interface will dynamically display configuration options based on the selected mapping type and visual property.

Backend Support for Attribute Mapping — Backend services that read graph attributes, validate mapping configurations sent by the frontend, compute Cytoscape-compatible style rules, and store them via the existing layout update API. This component integrates with the existing update_layout controller and the style_json storage mechanism — requiring no new database tables.

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Data: Google Summer of Code public archive. This project in the official archive