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

Abstract

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 paper, we introduce DiVA – Di ffusion V isualization and A nalysis, 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.

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“…Information diffusion is the process by which information spreads from one place to another. This includes the spread of rumors, memes, and ideas that could be spread differently by space, time, and subject through social networks [WLY*14, XTYL18, WLC*16, SGM*21, LWD*17]. Visual analytics approaches have been studied for many years to find insights [CWV*17, ZCW*14] and understand causality patterns [WLS*18].…”