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Weaving (Bio)Fabric: A Benchmark for Scalability Through Progressive Sampling Methods

Authors Alessandro Palma , Sara Di Bartolomeo , Marco Angelini

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Subject Classification

ACM Subject Classification
  • Human-centered computing → Graph drawings
Keywords
  • Progressive visualization
  • Biofabric
  • Layout
  • Alternative Network Visualization

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Abstract

Network data is widely used to represent complex systems across domains, enabling the discovery of structural patterns and relationships. However, as graphs grow in size, visualizing them effectively becomes increasingly challenging. BioFabric offers a compact representation in which nodes are drawn as horizontal lines and edges as vertical lines, eliminating edge crossings by design and supporting the identification of meaningful motifs such as staircases. Despite these advantages, the readability of BioFabric layouts depends critically on the ordering of nodes and edges, making both computation and visualization difficult to scale to high cardinality data. To address these challenges, we explore progressive visualization, a paradigm that allows users to inspect increasingly accurate partial results as data processing continues. Applying progressive visualization to BioFabric introduces a key difficulty: intermediate layouts may contain correct subsets of the data yet remain visually misleading if their ordering diverges significantly from the final layout. We frame this problem as one of ordering convergence to investigate how different sampling strategies affect the evolution of the layout. Specifically, we design a benchmark to evaluate how quickly intermediate visualizations approximate the final BioFabric ordering using measures such as Kendall-τ, and analyze the impact of progressive computation on the emergence of recognizable patterns, such as staircases. Our results provide a first characterization of how graph topology and progressive data chunking strategies influence convergence and layout accuracy. We derive best practices for designing progressive BioFabric systems that become informative before the full computation is complete. Two case studies show the benefits of progressively sampling BioFabric.

Cite As Get BibTex

Alessandro Palma, Sara Di Bartolomeo, and Marco Angelini. Weaving (Bio)Fabric: A Benchmark for Scalability Through Progressive Sampling Methods. In 34th International Symposium on Graph Drawing and Network Visualization (GD 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 396, pp. 23:1-23:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.GD.2026.23

Author Details

Alessandro Palma
  • Sapienza University of Rome, Italy
Sara Di Bartolomeo
  • TU Wien, Austria
Marco Angelini
  • Link Campus University, Rome, Italy

Funding

  • Di Bartolomeo, Sara: funded by the Austrian Science Fund (FWF) [10.55776/ESP513] "Simultaneous Multiprojection Graph Visualization".
  • Angelini, Marco: supported by the Ministero delle Imprese e del Made in Italy (MIMIT) under the Programma Nazionale Ricerca, Innovazione e Competitività per la transizione verde e digitale 2021-2027 - project "Smart ESG aNalytics for Transparency, Inclusivity & aLignment -– SENTINEL", Grant number: F/380011/01-03/X77 CUP: B69J25000300005

Supplementary Materials

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