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arxiv: 2604.12624 · v1 · submitted 2026-04-14 · 💻 cs.HC

Recognition: unknown

GraphTide: Augmenting Knowledge-Intensive Text with Progressive Nested Graph

Xin Qian , Dazhen Deng , Zhaoping He , Xingbo Wang , Yuchen He , Yingcai Wu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:32 UTC · model grok-4.3

classification 💻 cs.HC
keywords knowledge-intensive textnested graphsentity-relationship visualizationprogressive animationtext comprehensiongraph layoutuser studyvisualization technique
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The pith

Progressive animated nested graphs improve reader comprehension of knowledge-intensive texts over static methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GraphTide as a visualization system that turns dense texts into nested entity-relationship graphs built incrementally with animation. An on-demand pipeline decomposes sentences to capture both within-sentence and across-sentence links, while a structure-aware layout keeps the growing graph readable. Sentences and entities appear in sequence through smooth transitions so readers do not lose the narrative thread. A user study found participants understood the material better with this approach than with conventional graphs or fixed nested diagrams. The technique targets texts like scientific articles where many interconnected facts make unaided reading effortful.

Core claim

GraphTide progressively constructs nested entity-relationship graphs with an on-demand decomposition pipeline for intra- and inter-sentence relationships, applies a structure-aware force-directed layout optimization, and reveals content through animated incremental transitions, leading to significantly higher user comprehension scores than traditional graph techniques or static nested graph representations.

What carries the argument

On-demand entity-relationship decomposition pipeline that builds nested graphs for intra- and inter-sentence relationships, combined with animated progressive revelation and structure-aware force-directed layout.

If this is right

  • Readers can track entity connections more reliably as the text narrative unfolds without losing overall context.
  • Structural clarity of the growing graph improves through the layout optimization step.
  • Comprehension gains hold relative to both conventional graph visualizations and non-animated nested graphs.
  • The method supports knowledge-intensive domains such as academic articles where entity relationships span multiple sentences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same progressive nesting idea could be tested on other dense relational texts such as legal documents or technical reports.
  • Combining the pipeline with existing text-analysis tools might allow the graphs to update automatically when new sections are read.
  • The animation component could be examined for effects on reading speed in addition to comprehension accuracy.

Load-bearing premise

The automatic decomposition pipeline accurately extracts and structures entity relationships without introducing misrepresentations, and the animations do not add new cognitive burdens.

What would settle it

A replication user study with the same texts and tasks that finds no statistically significant comprehension difference between GraphTide and the static or traditional graph baselines.

Figures

Figures reproduced from arXiv: 2604.12624 by Dazhen Deng, Xingbo Wang, Xin Qian, Yingcai Wu, Yuchen He, Zhaoping He.

Figure 1
Figure 1. Figure 1: GraphTide supports the progressive understanding of knowledge-intensive text by incrementally constructing and displaying [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Usage Scenario. Alex begins with a blank canvas, where each click triggers the progressive rendering of the next sentence [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of Single-Layer and Nested Graph Representations. A single-layer graph either captures only the top-level [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sentence Decomposition Pipeline and Corresponding Visualization: The decomposition pipeline performs multiple rounds of [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A schematic diagram of the five forces defined in the force-directed process [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The Process of Progressive Presentation with Animation: Before a sentence mentioning “the local economy” appears, the [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: When the user hovers over a node, whether it’s an atomic node (A1) or a composite node (B1), the node and its connected [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: When a user clicks on an entry of the frequent entity list, the corresponding node and its connected nodes are highlighted in [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: User Performance measured using three quantitative metrics: (A), (B) and (C) shows the mean values of reading time, response [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Participants’ responses to various aspects of text comprehension support in GraphTide, Static Display, and Graphologue, [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
read the original abstract

Knowledge-intensive text usually contains fruitful entities and complex relationships, such as academic articles and scientific exposition. Reading and comprehending such texts often demands considerable time and mental effort to track the relationships between entities. To reduce the burden, we present GraphTide, a visualization technique that progressively constructs nested entity-relationship graphs with animation to support the understanding of complex text. Our method features an on-demand entity-relationship decomposition pipeline that constructs nested graphs to represent intra- and inter-sentence relationships. Moreover, we propose a structure-aware force-directed layout optimization algorithm to enhance structural clarity. Sentences and their associated entities are incrementally revealed through animated transitions, helping users maintain context as the narrative unfolds. A user study shows that GraphTide significantly improves users' comprehension of knowledge-intensive texts compared to traditional graph-based techniques and static nested graph representations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents GraphTide, a visualization technique that augments knowledge-intensive texts (e.g., academic articles) with progressively constructed nested entity-relationship graphs. Key components include an on-demand decomposition pipeline for intra- and inter-sentence relationships, a structure-aware force-directed layout algorithm, and animated incremental revelation of sentences and entities to maintain narrative context. The central claim is that a user study demonstrates GraphTide significantly improves user comprehension compared to traditional graph-based techniques and static nested graph representations.

Significance. If the empirical results hold under scrutiny, GraphTide offers a novel approach to reducing cognitive load in reading complex texts by combining nested graph structures with progressive animation. This could have practical value in HCI applications for education, scientific literature review, and information visualization, extending prior work on graph-based text augmentation with dynamic, context-preserving presentation.

major comments (2)
  1. [User study / results] User study section (details referenced in abstract and results): The claim of 'significant improvement' in comprehension lacks any description of study design elements including participant count, recruitment method, demographics, exact baseline implementations (how traditional graphs and static nested graphs were realized), comprehension metrics (e.g., accuracy on relationship questions, reading time, NASA-TLX scores), statistical tests (t-tests, ANOVA, p-values), or effect sizes. This information is required to evaluate whether the data support the central claim.
  2. [Methods / pipeline description] Entity-relationship decomposition pipeline (described in methods): No validation is reported for the on-demand pipeline's accuracy in capturing intra- and inter-sentence relationships, such as comparison to expert-annotated ground truth, precision/recall metrics, or inter-rater reliability. This directly affects the weakest assumption that the generated nested graphs faithfully represent the text without introducing misrepresentations.
minor comments (2)
  1. [Figures] Figure captions for the progressive animation examples could include more detail on what specific relationships are being revealed in each transition step to aid reader understanding.
  2. [Notation / layout] Notation for graph elements (nodes as entities, edges as relationships) is introduced but not consistently referenced with the same symbols across the layout optimization and animation sections.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and commit to substantial revisions that will strengthen the empirical and methodological sections.

read point-by-point responses

  1. Referee: [User study / results] User study section (details referenced in abstract and results): The claim of 'significant improvement' in comprehension lacks any description of study design elements including participant count, recruitment method, demographics, exact baseline implementations (how traditional graphs and static nested graphs were realized), comprehension metrics (e.g., accuracy on relationship questions, reading time, NASA-TLX scores), statistical tests (t-tests, ANOVA, p-values), or effect sizes. This information is required to evaluate whether the data support the central claim.

    Authors: We agree that the current description of the user study is insufficient for full evaluation and reproducibility. The manuscript summarized the key outcome to emphasize the visualization contribution, but this led to omission of critical design details. In the revised version we will expand the User Study section with a complete account of participant count and demographics, recruitment procedure, precise implementations of the two baseline conditions (traditional graph-based techniques and static nested graph representations), the comprehension metrics employed (including accuracy on relationship questions, reading time, and NASA-TLX workload scores), the statistical tests performed, associated p-values, and effect sizes. These additions will directly support the claim of significant improvement. revision: yes

  2. Referee: [Methods / pipeline description] Entity-relationship decomposition pipeline (described in methods): No validation is reported for the on-demand pipeline's accuracy in capturing intra- and inter-sentence relationships, such as comparison to expert-annotated ground truth, precision/recall metrics, or inter-rater reliability. This directly affects the weakest assumption that the generated nested graphs faithfully represent the text without introducing misrepresentations.

    Authors: We acknowledge that the absence of quantitative validation for the decomposition pipeline is a limitation that weakens confidence in the generated graphs. The pipeline combines dependency parsing and coreference resolution to extract intra- and inter-sentence relationships on demand, yet no accuracy assessment was included. In the revision we will add a new subsection reporting validation results: precision/recall against expert-annotated ground-truth texts drawn from the same domain as the study materials, together with inter-rater reliability statistics where multiple annotators were used. This will substantiate that the nested graphs faithfully capture the source text. revision: yes

Circularity Check

0 steps flagged

No circularity: design technique plus empirical user study

full rationale

The paper introduces GraphTide as a visualization technique featuring an on-demand entity-relationship decomposition pipeline, structure-aware force-directed layout, and animated progressive nested graphs. Its central claim rests on a comparative user study measuring comprehension improvements. No mathematical derivations, predictions, fitted parameters, or first-principles results are present that could reduce to inputs by construction. No self-citation load-bearing steps, uniqueness theorems, or ansatzes appear in the argument. The contribution is self-contained as an applied design plus external empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model, free parameters, or axioms are present; the work rests on standard HCI design practices and an empirical user study.

pith-pipeline@v0.9.0 · 5451 in / 986 out tokens · 68613 ms · 2026-05-10T15:32:28.403534+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

64 extracted references · 49 canonical work pages · 1 internal anchor

  1. [1]

    Shaaron Ainsworth. 2006. DeFT: A conceptual framework for learning with multiple representations. Learning and Instruction, 16, 183-198.Learning and Instruction16 (06 2006), 183–198. doi:10.1016/j.learninstruc.2006.03.001

    work page doi:10.1016/j.learninstruc.2006.03.001 2006

  2. [2]

    Mohammad Alharbi and Robert S. Laramee. 2019. SoS TextVis: An Extended Survey of Surveys on Text Visualization.Comput.8, 1 (2019), 17. doi:10.3390/COMPUTERS8010017

    work page doi:10.3390/computers8010017 2019

  3. [3]

    2003.Logics of conversation

    Nicholas Asher and Alex Lascarides. 2003.Logics of conversation. Cambridge University Press

    2003

  4. [4]

    Seversky

    Matthew Berger, Katherine McDonough, and Lee M. Seversky. 2017. cite2vec: Citation-Driven Document Exploration via Word Embeddings.IEEE Trans. Vis. Comput. Graph.23, 1 (2017), 691–700. doi:10.1109/TVCG.2016.2598667

    work page doi:10.1109/tvcg.2016.2598667 2017

  5. [5]

    Rachel M Best, Michael Rowe, Yasuhiro Ozuru, and Danielle S McNamara. 2005. Deep-level comprehension of science texts: The role of the reader and the text.Topics in language disorders25, 1 (2005), 65–83

    2005

  6. [6]

    Eric A Bier, Edward W Ishak, and Ed Chi. 2006. Entity workspace: an evidence file that aids memory, inference, and reading. InIntelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006. Proceedings

    2006

  7. [7]

    Matthew Brehmer, Stephen Ingram, Jonathan Stray, and Tamara Munzner. 2014. Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists.IEEE Trans. Vis. Comput. Graph.20, 12 (2014), 2271–2280. doi:10.1109/TVCG.2014.2346431

    work page doi:10.1109/tvcg.2014.2346431 2014

  8. [8]

    Bruce K Britton and Sami Gülgöz. 1991. Using Kintsch’s computational model to improve instructional text: Effects of repairing inference calls on recall and cognitive structures.Journal of educational Psychology83, 3 (1991), 329

    1991

  9. [9]

    Quim Castellà and Charles Sutton. 2014. Word storms: multiples of word clouds for visual comparison of documents. In23rd International World Wide Web Conference, WWW ’14, Seoul, Republic of Korea, April 7-11, 2014. ACM, 665–676. doi:10.1145/2566486.2567977

    work page doi:10.1145/2566486.2567977 2014

  10. [10]

    Wei Chen, Tianyi Lao, Jing Xia, Xinxin Huang, Biao Zhu, Wanqi Hu, and Huihua Guan. 2016. GameFlow: Narrative Visualization of NBA Basketball Games.IEEE Trans. Multim.18, 11 (2016), 2247–2256. doi:10.1109/TMM.2016.2614221

    work page doi:10.1109/tmm.2016.2614221 2016

  11. [11]

    Ming-Te Chi, Shih-Syun Lin, Shiang-Yi Chen, Chao-Hung Lin, and Tong-Yee Lee. 2015. Morphable Word Clouds for Time-Varying Text Data Visualization.IEEE Trans. Vis. Comput. Graph.21, 12 (2015), 1415–1426. doi:10.1109/TVCG.2015.2440241

    work page doi:10.1109/tvcg.2015.2440241 2015

  12. [12]

    Zhou, and Huamin Qu

    Weiwei Cui, Yingcai Wu, Shixia Liu, Furu Wei, Michelle X. Zhou, and Huamin Qu. 2010. Context-Preserving, Dynamic Word Cloud Visualization. IEEE Computer Graphics and Applications30, 6 (2010), 42–53. doi:10.1109/MCG.2010.102

    work page doi:10.1109/mcg.2010.102 2010

  13. [13]

    Dagre. 2015. Dagre: A Directed Graph Layout Library. https://github.com/dagrejs/dagre. Accessed: 2025-02-21

    2015

  14. [14]

    Ugur Dogrusöz, Erhan Giral, Ahmet Cetintas, Ali Civril, and Emek Demir. 2004. A Compound Graph Layout Algorithm for Biological Pathways. In Graph Drawing, 12th International Symposium, GD 2004, New York, NY, USA, September 29 - October 2, 2004, Revised Selected Papers (Lecture Notes in Computer Science, Vol. 3383). 442–447. doi:10.1007/978-3-540-31843-9_45

    work page doi:10.1007/978-3-540-31843-9_45 2004

  15. [15]

    Ugur Dogrusöz, Erhan Giral, Ahmet Cetintas, Ali Civril, and Emek Demir. 2009. A layout algorithm for undirected compound graphs.Inf. Sci.179, 7 (2009), 980–994. doi:10.1016/J.INS.2008.11.017

    work page doi:10.1016/j.ins.2008.11.017 2009

  16. [16]

    FMS Advanced Systems Group. 2007. Sentinel Visualizer. Website. https://sentinelvisualizer.com/

    2007

  17. [17]

    Michael Forster. 2002. Applying Crossing Reduction Strategies to Layered Compound Graphs. InGraph Drawing, 10th International Symposium, GD 2002, Irvine, CA, USA, August 26-28, 2002, Revised Papers (Lecture Notes in Computer Science, Vol. 2528), Stephen G. Kobourov and Michael T. Goodrich (Eds.). 276–284. doi:10.1007/3-540-36151-0_26

    work page doi:10.1007/3-540-36151-0_26 2002

  18. [18]

    Thomas MJ Fruchterman and Edward M Reingold. 1991. Graph drawing by force-directed placement.Software: Practice and experience21, 11 (1991), 1129–1164

    1991

  19. [19]

    Johanna Fulda, Matthew Brehmer, and Tamara Munzner. 2016. TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text. IEEE Trans. Vis. Comput. Graph.22, 1 (2016), 300–309. doi:10.1109/TVCG.2015.2467531 Manuscript submitted to ACM GraphTide: Augmenting Knowledge-Intensive Text with Progressive Nested Graph 25

    work page doi:10.1109/tvcg.2015.2467531 2016

  20. [20]

    Hongchao Gu, Dexun Li, Kuicai Dong, Hao Zhang, Hang Lv, Hao Wang, Defu Lian, Yong Liu, and Enhong Chen. 2025. Rapid: Efficient retrieval- augmented long text generation with writing planning and information discovery.arXiv preprint arXiv:2503.00751(2025)

    work page arXiv 2025

  21. [21]

    In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24)

    Ziwei Gu, Ian Arawjo, Kenneth Li, Jonathan K. Kummerfeld, and Elena L. Glassman. 2024. An AI-Resilient Text Rendering Technique for Reading and Skimming Documents. InProceedings of the CHI Conference on Human Factors in Computing Systems, CHI 2024, Honolulu, HI, USA, May 11-16, 2024, Florian ’Floyd’ Mueller, Penny Kyburz, Julie R. Williamson, Corina Sas, ...

    work page doi:10.1145/3613904.3642699 2024

  22. [22]

    Boedihardjo, and Naren Ramakrishnan

    Mahmud Shahriar Hossain, Patrick Butler, Arnold P. Boedihardjo, and Naren Ramakrishnan. 2012. Storytelling in entity networks to support intelligence analysts. InThe 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, Beijing, China, August 12-16, 2012, Qiang Yang, Deepak Agarwal, and Jian Pei (Eds.). ACM, 1375–1383. ...

    work page doi:10.1145/2339530.2339742 2012

  23. [23]

    Chao-Wen Hsuan Yuan, Tzu-Wei Yu, Jia-Yu Pan, and Wen-Chieh Lin. 2024. KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based Guidance.IEEE Transactions on Visualization and Computer Graphics30, 12 (2024), 7702–7716. doi:10.1109/TVCG.2024.3360690

    work page doi:10.1109/tvcg.2024.3360690 2024

  24. [24]

    Mengdie Hu, Krist Wongsuphasawat, and John T. Stasko. 2017. Visualizing Social Media Content with SentenTree.IEEE Trans. Vis. Comput. Graph. 23, 1 (2017), 621–630. doi:10.1109/TVCG.2016.2598590

    work page doi:10.1109/tvcg.2016.2598590 2017

  25. [25]

    Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, and Wen-mei Hwu. 2022. DEER: Descriptive knowledge graph for explaining entity relationships.arXiv preprint arXiv:2205.10479(2022)

    work page arXiv 2022

  26. [26]

    Franziska Huth, Maurice Koch, Miriam Awad-Mohammed, Daniel Weiskopf, and Kuno Kurzhals. 2024. Eye tracking on text reading with visual enhancements. InProceedings of the 2024 Symposium on Eye Tracking Research and Applications. 1–7

    2024

  27. [27]

    Magdalena Jankowska, Vlado Keselj, and Evangelos E. Milios. 2012. Relative N-gram signatures: Document visualization at the level of character N-grams. In7th IEEE Conference on Visual Analytics Science and Technology, IEEE V AST 2012, Seattle, W A, USA, October 14-19, 2012. IEEE Computer Society, 103–112. doi:10.1109/VAST.2012.6400484

    work page doi:10.1109/vast.2012.6400484 2012

  28. [28]

    Dow, and Haijun Xia

    Peiling Jiang, Jude Rayan, Steven P. Dow, and Haijun Xia. 2023. Graphologue: Exploring Large Language Model Responses with Interactive Diagrams. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. Article 3, 20 pages. doi:10.1145/3586183.3606737

    work page doi:10.1145/3586183.3606737 2023

  29. [29]

    Tomihisa Kamada and Satoru Kawai. 1989. An Algorithm for Drawing General Undirected Graphs.Inf. Process. Lett.31, 1 (1989), 7–15. doi:10.1016/0020- 0190(89)90102-6

    work page doi:10.1016/0020- 1989

  30. [30]

    Bederson

    Hyunmo Kang, Catherine Plaisant, Bongshin Lee, and Benjamin B. Bederson. 2006. NetLens: Iterative Exploration of Content-Actor Network Data. In1st IEEE Symposium On Visual Analytics Science And Technology, IEEE V AST 2006, Baltimore, MD, USA, October 31 - November 2, 2006, Pak Chung Wong and Daniel A. Keim (Eds.). IEEE Computer Society, 91–98. doi:10.1109...

    work page doi:10.1109/vast.2006.261426 2006

  31. [31]

    Michael Kaufmann. 2022. The lokahi prototype: Toward the automatic extraction of entity relationship models from text.arXiv preprint arXiv:2201.05327(2022)

    work page arXiv 2022

  32. [32]

    Walter Kintsch and Teun A Van Dijk. 1978. Toward a model of text comprehension and production.Psychological review85, 5 (1978), 363

    1978

  33. [33]

    Yao-Ting Sung Kuo-En Chang and Ine-Dai Chen. 2002. The Effect of Concept Mapping to Enhance Text Comprehension and Summarization.The Journal of Experimental Education71, 1 (2002), 5–23. doi:10.1080/00220970209602054

    work page doi:10.1080/00220970209602054 2002

  34. [34]

    Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, and Huamin Qu. 2022. KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation.IEEE Transactions on Visualization and Computer Graphics28, 1 (2022), 195–205. doi:10.1109/TVCG.2021.3114863

    work page doi:10.1109/tvcg.2021.3114863 2022

  35. [35]

    Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, and Junping Du. 2020. Leveraging Graph to Improve Abstractive Multi-Document Summarization. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020. Association for Computational Linguistics, 6232–6243. doi:10.18653/V1/2020.ACL-MAIN.555

    work page doi:10.18653/v1/2020.acl-main.555 2020

  36. [36]

    MANN and SANDRA A

    WILLIAM C. MANN and SANDRA A. THOMPSON. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization.Text - Interdisciplinary Journal for the Study of Discourse8, 3 (1988), 243–281. doi:doi:10.1515/text.1.1988.8.3.243

    work page doi:10.1515/text.1.1988.8.3.243 1988

  37. [37]

    Danielle S McNamara and Walter Kintsch. 1996. Learning from texts: Effects of prior knowledge and text coherence.Discourse processes22, 3 (1996), 247–288

    1996

  38. [38]

    Mindjet. 1999. MindManager – Intuitive Visualization Tools. Website. https://www.mindmanager.com/

    1999

  39. [39]

    Jaume Nualart Vilaplana, Mario Pérez-Montoro, and Mitchell Whitelaw. 2014. How we draw texts: A review of approaches to text visualization and exploration.El Profesional de la Informacion23 (05 2014), 221–235. doi:10.3145/epi.2014.may.02

    work page doi:10.3145/epi.2014.may.02 2014

  40. [40]

    Daniela Oelke and Iryna Gurevych. 2014. A study on human-generated tag structures to inform tag cloud layout. InInternational Working Conference on Advanced Visual Interfaces, A VI 2014, Como, Italy, May 27-29, 2014. ACM, 297–304. doi:10.1145/2598153.2598155

    work page doi:10.1145/2598153.2598155 2014

  41. [41]

    OpenAI. 2024. GPT-4o System Card.arXiv preprint arXiv: 2410.21276(2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  42. [42]

    Zhou, Yangqiu Song, Weihong Qian, Fei Wang, and Shixia Liu

    Shimei Pan, Michelle X. Zhou, Yangqiu Song, Weihong Qian, Fei Wang, and Shixia Liu. 2013. Optimizing temporal topic segmentation for intelligent text visualization. In18th International Conference on Intelligent User Interfaces, IUI 2013, Santa Monica, CA, USA, March 19-22, 2013. ACM, 339–350. doi:10.1145/2449396.2449441

    work page doi:10.1145/2449396.2449441 2013

  43. [43]

    Fernando Vieira Paulovich and Rosane Minghim. 2008. HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections.IEEE Trans. Vis. Comput. Graph.14, 6 (2008), 1229–1236. doi:10.1109/TVCG.2008.138

    work page doi:10.1109/tvcg.2008.138 2008

  44. [44]

    Teresa Schurer, Bertram Opitz, and Torsten Schubert. 2020. Working Memory Capacity but Not Prior Knowledge Impact on Readers’ Attention and Text Comprehension.Frontiers in Education5 (03 2020). doi:10.3389/feduc.2020.00026

    work page doi:10.3389/feduc.2020.00026 2020

  45. [45]

    Zohreh Sharafi, Alessandro Marchetto, Angelo Susi, Giuliano Antoniol, and Yann-Gaël Guéhéneuc. 2013. An empirical study on the efficiency of graphical vs. textual representations in requirements comprehension. In2013 21st International Conference on Program Comprehension (ICPC). IEEE, Manuscript submitted to ACM 26 Qian et al. 33–42

    2013

  46. [46]

    Aaron Springer and Steve Whittaker. 2019. Progressive disclosure: empirically motivated approaches to designing effective transparency. In Proceedings of the 24th international conference on intelligent user interfaces. 107–120

    2019

  47. [47]

    Stasko, Carsten Görg, Zhicheng Liu, and Kanupriya Singhal

    John T. Stasko, Carsten Görg, Zhicheng Liu, and Kanupriya Singhal. 2007. Jigsaw: Supporting Investigative Analysis through Interactive Visualization. In2nd IEEE Symposium on Visual Analytics Science and Technology, IEEE V AST 2007, Sacramento, CA,USA, October 30 - November 1, 2007. IEEE Computer Society, 131–138. doi:10.1109/VAST.2007.4389006

    work page doi:10.1109/vast.2007.4389006 2007

  48. [48]

    Florian Stoffel, Wolfgang Jentner, Michael Behrisch, Johannes Fuchs, and Daniel A. Keim. 2017. Interactive Ambiguity Resolution of Named Entities in Fictional Literature.Comput. Graph. Forum36, 3 (2017), 189–200. doi:10.1111/CGF.13179

    work page doi:10.1111/cgf.13179 2017

  49. [49]

    Seifert, Priti Shah, and Eytan Adar

    Hariharan Subramonyam, Colleen M. Seifert, Priti Shah, and Eytan Adar. 2020. texSketch: Active Diagramming through Pen-and-Ink Annotations. InCHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, Regina Bernhaupt, Florian ’Floyd’ Mueller, David Verweij, Josh Andres, Joanna McGrenere, Andy Cockburn, Ignacio Av...

    work page doi:10.1145/3313831.3376155 2020

  50. [50]

    Kozo Sugiyama and Kazuo Misue. 1991. Visualization of structural information: automatic drawing of compound digraphs.IEEE Trans. Syst. Man Cybern.21, 4 (1991), 876–892. doi:10.1109/21.108304

    work page doi:10.1109/21.108304 1991

  51. [52]

    Kozo Sugiyama and Kazuo Misue. 1995. A Generic Compound Graph Visualizer/Manipulator: D-ABDUCTOR. InGraph Drawing, Symposium on Graph Drawing, GD ’95, Passau, Germany, September 20-22, 1995, Proceedings (Lecture Notes in Computer Science, Vol. 1027), Franz-Josef Brandenburg (Ed.). 500–503. doi:10.1007/BFB0021834

    work page doi:10.1007/bfb0021834 1995

  52. [53]

    Kozo Sugiyama, Shojiro Tagawa, and Mitsuhiko Toda. 1981. Methods for visual understanding of hierarchical system structures.IEEE Transactions on Systems, Man, and Cybernetics11, 2 (1981), 109–125

    1981

  53. [54]

    Ashley Suh, Mustafa Hajij, Bei Wang, Carlos Scheidegger, and Paul Rosen. 2020. Persistent Homology Guided Force-Directed Graph Layouts.IEEE Trans. Vis. Comput. Graph.26, 1 (2020), 697–707. doi:10.1109/TVCG.2019.2934802

    work page doi:10.1109/tvcg.2019.2934802 2020

  54. [55]

    Sangho Suh, Bryan Min, Srishti Palani, and Haijun Xia. 2023. Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST 2023, San Francisco, CA, USA, 29 October 2023- 1 November 2023. 1:1–1:18. doi:10.1145/3586183.3606756

    work page doi:10.1145/3586183.3606756 2023

  55. [56]

    Guodao Sun, Yingcai Wu, Shixia Liu, Tai-Quan Peng, Jonathan J. H. Zhu, and Ronghua Liang. 2014. EvoRiver: Visual Analysis of Topic Coopetition on Social Media.IEEE Trans. Vis. Comput. Graph.20, 12 (2014), 1753–1762. doi:10.1109/TVCG.2014.2346919

    work page doi:10.1109/tvcg.2014.2346919 2014

  56. [57]

    Stuffer, Y

    Danielle Albers Szafir, D. Stuffer, Y. Sohail, and Michael Gleicher. 2016. TextDNA: Visualizing Word Usage with Configurable Colorfields.Comput. Graph. Forum35, 3 (2016), 421–430. doi:10.1111/CGF.12918

    work page doi:10.1111/cgf.12918 2016

  57. [58]

    Tashman and W

    Craig S. Tashman and W. Keith Edwards. 2011. LiquidText: a flexible, multitouch environment to support active reading. InProceedings of the International Conference on Human Factors in Computing Systems, CHI 2011, Vancouver, BC, Canada, May 7-12, 2011, Desney S. Tan, Saleema Amershi, Bo Begole, Wendy A. Kellogg, and Manas Tungare (Eds.). ACM, 3285–3294. d...

    work page doi:10.1145/1978942.1979430 2011

  58. [59]

    Frank van Ham, Martin Wattenberg, and Fernanda B. Viégas. 2009. Mapping Text with Phrase Nets.IEEE Trans. Vis. Comput. Graph.15, 6 (2009), 1169–1176. doi:10.1109/TVCG.2009.165

    work page doi:10.1109/tvcg.2009.165 2009

  59. [60]

    Xingbo Wang, Renfei Huang, Zhihua Jin, Tianqing Fang, and Huamin Qu. 2024. CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models.IEEE Transactions on Visualization and Computer Graphics30, 1 (2024), 273–283. doi:10.1109/ TVCG.2023.3327153

    work page arXiv 2024

  60. [61]

    Martin Wattenberg and Fernanda B. Viégas. 2008. The Word Tree, an Interactive Visual Concordance.IEEE Trans. Vis. Comput. Graph.14, 6 (2008), 1221–1228. doi:10.1109/TVCG.2008.172

    work page doi:10.1109/tvcg.2008.172 2008

  61. [62]

    Krist Wongsuphasawat and David Gotz. 2012. Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization. IEEE Trans. Vis. Comput. Graph.18, 12 (2012), 2659–2668. doi:10.1109/TVCG.2012.225

    work page doi:10.1109/tvcg.2012.225 2012

  62. [63]

    William Wright, David Schroh, Pascale Proulx, Alex Skaburskis, and Brian Cort. 2006. The Sandbox for analysis: concepts and methods. InProceedings of the SIGCHI conference on Human Factors in computing systems. 801–810

    2006

  63. [64]

    Yingcai Wu, Shixia Liu, Kai Yan, Mengchen Liu, and Fangzhao Wu. 2014. OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media.IEEE Trans. Vis. Comput. Graph.20, 12 (2014), 1763–1772. doi:10.1109/TVCG.2014.2346920

    work page doi:10.1109/tvcg.2014.2346920 2014

  64. [65]

    Youfu Yan, Yu Hou, Yongkang Xiao, Rui Zhang, and Qianwen Wang. 2025. KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph Integration.IEEE Transactions on Visualization and Computer Graphics31, 1 (2025), 547–557. doi:10.1109/TVCG.2024.3456364 Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009 Manuscript submitted to ACM

    work page doi:10.1109/tvcg.2024.3456364 2025