research-article

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

Authors:

Tanmoy ChakrabortyAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 4

Article No.: 47, Pages 1 - 33

https://doi.org/10.1145/3558771

Published: 24 February 2023 Publication History

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 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.

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Cited By

Gong YShang LWang D(2024)Integrating Social Explanations Into Explainable Artificial Intelligence (XAI) for Combating Misinformation: Vision and ChallengesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340423611:5(6705-6726)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3404236
Noi ERudolph ADodge S(2023)VASA: an exploratory visualization tool for mapping spatio-temporal structure of mobility – a COVID-19 case studyCartography and Geographic Information Science10.1080/15230406.2022.2156388(1-22)Online publication date: 21-Feb-2023
https://doi.org/10.1080/15230406.2022.2156388

Index Terms

DiVA: A Scalable, Interactive and Customizable Visual Analytics Platform for Information Diffusion on Large Networks
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization
      2. Visual analytics

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 4

May 2023

364 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3583065

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 24 February 2023

Online AM: 24 August 2022

Accepted: 06 August 2022

Revised: 02 August 2022

Received: 10 December 2021

Published in TKDD Volume 17, Issue 4

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Cited By

Gong YShang LWang D(2024)Integrating Social Explanations Into Explainable Artificial Intelligence (XAI) for Combating Misinformation: Vision and ChallengesIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340423611:5(6705-6726)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3404236
Noi ERudolph ADodge S(2023)VASA: an exploratory visualization tool for mapping spatio-temporal structure of mobility – a COVID-19 case studyCartography and Geographic Information Science10.1080/15230406.2022.2156388(1-22)Online publication date: 21-Feb-2023
https://doi.org/10.1080/15230406.2022.2156388

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