DiVA: A Scalable, Interactive and Customizable Visual Analytics Platform for Information Diffusion on Large Networks

• Published in: ACM transactions on knowledge discovery from data 2023-02, Vol.17 (4), p.1-33, Article 47
• Main Authors: Sehnan, Dhruv, Goel, Vasu, Masud, Sarah, Jain, Chhavi, Goyal, Vikram, Chakraborty, Tanmoy
• Format: Article
• Language: English
• Subjects: Human-centered computing; Information visualization; Visual analytics
• ISSN: 1556-4681; 1556-472X
• DOI: 10.1145/3558771

With an increasing outreach of digital platforms in our lives, researchers have taken a keen interest in studying different facets of social interactions. Analyzing the spread of information (aka diffusion) has brought forth multiple research areas such as modelling user engagement, determining emerging topics, forecasting the virality of online posts and predicting information cascades. Despite such ever-increasing interest, there remains a vacuum among easy-to-use interfaces for large-scale visualization of diffusion models. In this article, we introduce DiVA—Diffusion Visualization and Analysis, a tool that provides a scalable web interface and extendable APIs to analyze various diffusion trends on networks. DiVA uniquely offers support for simultaneous comparison of two competing diffusion models and even the comparison with the ground-truth results, which help develop a coherent understanding of real-world scenarios. Along with performing an exhaustive feature comparison and system evaluation of DiVA against publicly-available web interfaces for information diffusion, we conducted a user study to understand the strengths and limitations of DiVA. We noticed that evaluators had a seamless user experience, especially when analyzing diffusion on large networks.