CFP - Special Issue: Bridging Graph Drawing and Dimensionality Reduction in Information Visualization
Andreas Kerren <[email protected]> Tue, 30 Jun 2026 16:04:30 +0000
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Apologies for cross-posting! ———————————————————————————————————————— Special Issue of Information Visualization Bridging Graph Drawing and Dimensionality Reduction in Information Visualization Submission Deadline: November 20, 2026 Journal Page: https://journals.sagepub.com/page/ivi/call_for_special_issue/bridging_graph ———————————————————————————————————————— Graph Drawing (GD) and Dimensionality Reduction (DR) are fundamental research areas in information visualization, both aiming to represent complex, high-dimensional, and relational data in interpretable low-dimensional spaces. Although these domains share common goals and methodological challenges, they have largely evolved in parallel, with limited exchange of techniques, evaluation methodologies, and theoretical foundations. This special issue aims to consolidate cutting-edge research emerging from the "GDxDR: Bridging Graph Drawing and Dimensionality Reduction Workshop" at EuroVis 2026, while inviting novel contributions that advance the theory, methods, and applications of integrated graph drawing and dimensionality reduction. To ensure diversity and quality, we welcome submissions from both workshop participants (with ≥30% new content) and external researchers, encouraging work on recent advances in graph drawing, dimensionality reduction, and their integration in information visualization. More specifically, graph- and embedding-based visual representations have become increasingly important for supporting exploration, explanation, and decision-making across a wide range of domains. Recent advances in visual analytics, machine learning, explainable artificial intelligence, and representation learning have created new opportunities to bridge GD and DR, while also raising important questions related to interpretability, scalability, trust, and evaluation. This special issue seeks to showcase the latest state-of-the-art research on the integration of GD and DR, emphasizing methodological innovation, empirical validation, and human-centered design. Topics of interest include, but are not limited to, the following: - Conceptual and methodological connections between graph drawing and dimensionality reduction - Evaluation methodologies, interpretability, and stability in layout and embedding techniques - Human-centered and perceptual aspects of GD and DR - Quality metrics, scalability, and reliability in visual representations - Hybrid or unified frameworks combining GD and DR principles - Visualization systems, tools, or applications demonstrating GD–DR integration - Learning-based, generative, and AI-assisted graph layouts and embeddings - Explainable and interpretable GD and DR methods - Trust, uncertainty, and bias in graph and projection-based visualizations *** Timeline *** All submitted manuscripts will go through the same rigorous peer-review process of the journal. The timeline of the special issue is as follows: - Submissions due: November 20, 2026 - Initial reviews due: January 31, 2027 - Revisions due: April 15, 2027 - Second round of review due (if needed): May 31, 2027 - Final acceptance: June 15, 2027 - Final versions due: July/August 2027 - Expected publication of the special issue: September/October 2027 Inquiries can be made to any of the guest editors. Please inform the guest editors of your intent to submit. For further author information and submission guidelines, please refer to https://journals.sagepub.com/author-instructions/IVI Final manuscript submissions should be made online through the journal submission system. *** Guest Editors *** Andreas Kerren Linköping University and Linnaeus University, Norrköping/Växjö, Sweden [email protected] Claudio D. G. Linhares Linnaeus University, Växjö, Sweden [email protected] Fernando V. Paulovich Eindhoven University of Technology, Eindhoven, Netherlands [email protected] Alessio Arleo Eindhoven University of Technology, Eindhoven, Netherlands [email protected] Rafael M. Martins Linnaeus University, Växjö, Sweden [email protected] ---------------------------------------------------------------------------------------- To unsubscribe from CHI-ANNOUNCEMENTS send an email to: mailto:[email protected] To manage your SIGCHI Mailing lists or read our polices see: https://sigchi.org/operations/listserv/ ----------------------------------------------------------------------------------------