VizChitra 2025

We will present Spiral Visualization. Spiral visualization is a powerful community-centric Visual analytic solution for undirected, unweighted, and static networks. It has three primary goals: (I) drawing large networks in a compact and intuitive format, (II) visualizing node centrality within network communities, and (III) visualizing both node and community properties.

Spiral visualization makes it easier to identify, interpret, and comprehend different network properties, such as (1) identifying the number of communities within the network, (2) visualizing and comparing the sizes of different communities, (3) visualizing and comparing the edge-density of communities (i.e., the ratio of actual links within a community to the total number of possible links), (4) identifying important or central nodes within communities, (5) understanding centrality distribution within communities, (6) comprehending connections between communities and (7) comprehending node connections. The following two videos briefly demonstrate Spiral Visualization \href{https://youtu.be/cvLdXAThIXY}{https://youtu.be/cvLdXAThIXY}

Spiral Visualization, published in IEEEVIS 2023, can represent networks with up to 10,000 nodes. The source code for the Spiral visualization is available https://github.com/Garima17/SpiralVisualization

{Mention 1-2 takeaways from your session}

1. Understanding a novel visual analytic solution for comprehending large networks: Gain insights into how spiral layouts can effectively represent complex networks. Learn how to make a visual solution scalable to accommodate large data. Understand the design considerations and implementation strategies behind creating effective spiral visualizations, including challenges faced and solutions developed during the research.
2. Applications in Real-world Scenarios: Explore practical applications of spiral visualizations in domains such as social network analysis, biological data interpretation, and more, demonstrating their versatility and effectiveness.

Who Will Benefit from This Talk?

1. Domain Experts & Analysts: Professionals working with large-scale network data (e.g., social networks, co-authorship graphs, biological networks) seeking innovative visualization techniques to uncover patterns and insights.

2. Visual Analytics Researchers: Academics and practitioners interested in exploring novel visual representations for complex, high-dimensional data.

3. Data Visualization Enthusiasts: Individuals keen on learning about cutting-edge visualization methods that enhance data interpretability and storytelling.

4. Dashboard Designers & UX Professionals: Designers aiming to integrate advanced visual elements into dashboards for improved user engagement and data comprehension.

5. Students & Educators in Data Science: Learners and teachers looking to expand their knowledge on scalable visualization techniques applicable to big data scenarios.

Your session will provide valuable insights into the development and application of scalable spiral visualizations, offering practical knowledge for effectively representing and analyzing complex networks.

Garima Jindal
Assistant Professor, BITS Pilani – Hyderabad Campus
Ph.D. in Computer Science and Engineering, IIIT Hyderabad
Email: garima.jindal@research.iiit.ac.in
LinkedIn: https://www.linkedin.com/in/garima-jindal-218a4437/

Garima Jindal has recently submitted her Ph.D. thesis in Computer Science and Engineering department at the International Institute of Information Technology (IIIT) Hyderabad, specializing in data visualization. Her research focuses on developing scalable visual analytics solutions for large-scale data. She holds a published patent titled “System and Method for Visualizing Large Graph Data Using Spirals” (US Patent App. 18/385,919). Her work has been presented at IEEE VIS. Beginning in May 2025, she will join BITS Pilani, Hyderabad Campus, as an Assistant Professor, where she will continue her research in visual analytics and network visualization.