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NNP-NET: Accelerating t-SNE Graph Drawing for Very Large Graphs by Neural Networks

Authors Ilan Hartskeerl , Tamara Mchedlidze , Simon van Wageningen , Peter Vangorp , Alexandru Telea

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ACM Subject Classification
  • Human-centered computing → Graph drawings
  • Computing methodologies → Neural networks
Keywords
  • supervised graph drawing
  • dimensionality reduction
  • t-SNE

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Abstract

tsNET is a recent graph drawing (GD) method that creates high quality layouts but suffers from a very high runtime. We present a new GD method, NNP-NET, which reduces tsNET’s time complexity to generate layouts for very large graphs in seconds. Additionally, we extend tsNET to support drawing graphs with edge weights. We accomplish this by replacing tsNET’s t-SNE projection with Neural Network Projection (NNP), a fast dimensionality reduction (DR) method that can imitate any given DR method. Our experiments show that NNP-NET gets good quality results when compared to other state-of-the art GD methods while yielding a better computational scalability.

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Ilan Hartskeerl, Tamara Mchedlidze, Simon van Wageningen, Peter Vangorp, and Alexandru Telea. NNP-NET: Accelerating t-SNE Graph Drawing for Very Large Graphs by Neural Networks. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 22:1-22:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.GD.2025.22

Author Details

Ilan Hartskeerl
  • Department of Information and Computing Sciences, Utrecht University, The Netherlands
Tamara Mchedlidze
  • Department of Information and Computing Sciences, Utrecht University, The Netherlands
Simon van Wageningen
  • Department of Information and Computing Sciences, Utrecht University, The Netherlands
Peter Vangorp
  • Department of Information and Computing Sciences, Utrecht University, The Netherlands
Alexandru Telea
  • Department of Information and Computing Sciences, Utrecht University, The Netherlands

Supplementary Materials

References

  1. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org. URL: https://doi.org/10.48550/arXiv.1603.04467.
  2. Reyan Ahmed, Felice De Luca, Sabin Devkota, Stephen Kobourov, and Mingwei Li. Graph drawing via gradient descent, (GD)². In Graph Drawing, pages 3-17. Springer, 2020. URL: https://doi.org/10.1007/978-3-030-68766-3_1.
  3. Reyan Ahmed, Felice De Luca, Sabin Devkota, Stephen Kobourov, and Mingwei Li. Multicriteria scalable graph drawing via stochastic gradient descent, (SGD)². IEEE TVCG, 28(6):2388-2399, 2022. URL: https://doi.org/10.1109/TVCG.2022.3155564.
  4. Ulrik Brandes and Christian Pich. Eigensolver methods for progressive multidimensional scaling of large data. In Graph Drawing, pages 42-53. Springer, 2007. URL: https://doi.org/10.1007/978-3-540-70904-6_6.
  5. Ulrik Brandes and Christian Pich. An experimental study on distance-based graph drawing. In Graph Drawing, pages 218-229. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-00219-9_21.
  6. Carlo Bredius, Zonglin Tian, and Alexandru C. Telea. Visual Exploration of Neural Network Projection Stability. In MLVis, 2022. URL: https://doi.org/10.2312/mlvis.20221068.
  7. David M. Chan, Roshan Rao, Forrest Huang, and John F. Canny. t-SNE-CUDA: GPU-accelerated t-SNE and its applications to modern data. In Computer Architecture and High Performance Computing, 2018. URL: https://doi.org/10.1109/CAHPC.2018.8645912.
  8. Markus Chimani, Carsten Gutwenger, Michael Jünger, Gunnar W. Klau, Karsten Klein, and Petra Mutzel. The Open Graph Drawing Framework (OGDF). In R. Tamassia, editor, Handbook of Graph Drawing and Visualization, pages 543-569. CRC Press, 2014. Google Scholar
  9. Timothy A. Davis and Yifan Hu. The University of Florida sparse matrix collection. ACM Transactions on Mathematical Software, 38(1):1-25, 2011. URL: https://doi.org/10.1145/2049662.2049663.
  10. Sabin Devkota, Reyan Ahmed, Felice De Luca, Katherine E. Isaacs, and Stephen Kobourov. Stress-plus-x (spx) graph layout. In Graph Drawing, pages 291-304. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-35802-0_23.
  11. Peter Eades. A heuristic for graph drawing. Congressus numerantium, 42:146-160, 1984. Google Scholar
  12. Peter Eades, Michael E. Houle, and Richard Webber. Finding the best viewpoints for three-dimensional graph drawings. In Graph Drawing. Springer, 1997. URL: https://doi.org/10.1007/3-540-63938-1_53.
  13. John Ellson, Emden R. Gansner, Lefteris Koutsofios, Stephen C. North, and Gordon Woodhull. Graphviz - open source graph drawing tools. In Graph Drawing, pages 483-484. Springer, 2002. URL: https://doi.org/10.1007/3-540-45848-4_57.
  14. Mateus Espadoto, Nina Hirata, Tomita Sumiko, and Alexandru C. Telea. Deep learning multidimensional projections. Information Visualization, 19(3):247-269, 2020. URL: https://doi.org/10.1177/1473871620909485.
  15. Mateus Espadoto, Rafael M. Martins, Andreas Kerren, Nina S. T. Hirata, and Alexandru C. Telea. Toward a quantitative survey of dimension reduction techniques. IEEE TVCG, 27(3):2153-2173, 2019. URL: https://doi.org/10.1109/TVCG.2019.2944182.
  16. Thomas M. J. Fruchterman and Edward M. Reingold. Graph drawing by force-directed placement. Software - Practice and Experience, 21(11):1129-1164, 1991. URL: https://doi.org/10.1002/spe.4380211102.
  17. Emden R. Gansner, Yifan Hu, and Stephen C. North. A maxent-stress model for graph layout. IEEE TVCG, 19(6):927-940, 2013. URL: https://doi.org/10.1109/TVCG.2012.299.
  18. Emden R. Gansner, Yehuda Koren, and Stephen C. North. Graph drawing by stress majorization. In Graph Drawing, pages 239-250. Springer, 2005. URL: https://doi.org/10.1007/978-3-540-31843-9_25.
  19. Helen Gibson, Joe Faith, and Paul Vickers. A survey of two-dimensional graph layout techniques for information visualisation. Information Visualization, 12(3-4):324-357, 2013. URL: https://doi.org/10.1177/1473871612455749.
  20. Loann Giovannangeli, Frederic Lalanne, David Auber, Romain Giot, and Romain Bourqui. Deep neural network for drawing networks, (DNN)². In Graph Drawing, pages 375-390. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-92931-2_27.
  21. Loann Giovannangeli, Frederic Lalanne, David Auber, Romain Giot, and Romain Bourqui. Toward efficient deep learning for graph drawing (dl4gd). IEEE TVCG, 30(2):1516-1532, 2024. URL: https://doi.org/10.1109/TVCG.2022.3222186.
  22. Martin Gronemann. Engineering the fast-multipole-multilevel method for multicore and SIMD architectures. Master’s thesis. Technische Univ. Dortmund, 2009. Google Scholar
  23. Florian Grötschla, Joël Mathys, Robert Veres, and Roger Wattenhofer. CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs. In ICLR, Vienna, Austria, May 2024. URL: https://openreview.net/forum?id=vtyasLn4RM.
  24. Kenneth M. Hall. An r-dimensional quadratic placement algorithm. Management science, 17(3):219-229, 1970. URL: https://doi.org/10.1287/mnsc.17.3.219.
  25. David Harel and Yehuda Koren. Graph drawing by high-dimensional embedding. JGAA, 8(2):195-214, 2004. URL: https://doi.org/10.7155/jgaa.00089.
  26. Ilan Hartskeerl, Tamara Mchedlidze, Simon van Wageningen, Peter Vangorp, and Alexandru Telea. NNP-NET. Software, (visited on 2025-11-07). URL: https://github.com/IlanHartskeerl/NNP-NET
    Software Heritage Logo archived version
    full metadata available at: https://doi.org/10.4230/artifacts.25058
  27. Yifan Hu. Efficient, high-quality force-directed graph drawing. Mathematica journal, 10(1):37-71, 2005. Google Scholar
  28. Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian. ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLOS ONE, 9(6):e98679, 2014. URL: https://doi.org/10.1371/journal.pone.0098679.
  29. Tomihisa Kamada and Satoru Kawai. An algorithm for drawing general undirected graphs. Inf Process Lett, 31(1):7-15, 1989. URL: https://doi.org/10.1016/0020-0190(89)90102-6.
  30. Johannes F. Kruiger, Paulo E. Rauber, Rafael M. Martins, Andreas Kerren, Stephen Kobourov, and Alexandru C. Telea. Graph layouts by t-SNE. Computer Graphics Forum, 36(3):283-294, 2017. URL: https://doi.org/10.1111/cgf.13187.
  31. Joseph B. Kruskal and Judith B. Seery. Designing network diagrams. In General Conference on Social Graphics, pages 22-50, 1980. Google Scholar
  32. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, volume 26, 2013. URL: https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html.
  33. Stephen C. North. Drawing graphs with NEATO, 2004. NEATO User’s Manual. Google Scholar
  34. Fernando V. Paulovich, Alessio Arleo, and Stef van den Elzen. When dimensionality reduction meets graph (drawing) theory: Introducing a common framework, challenges and opportunities. Computer Graphics Forum, 44(3):e70105, 2025. URL: https://doi.org/10.1111/cgf.70105.
  35. Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev, Thomas Hollt, Baldur van Lew, Boudewijn Lelieveldt, Elmar Eisemann, and Anna Vilanova. GPGPU linear complexity t-SNE optimization. IEEE TVCG, 26(1):1172-1181, 2020. URL: https://doi.org/10.1109/TVCG.2019.2934307.
  36. Helen C. Purchase. Metrics for graph drawing aesthetics. Journal of Visual Languages & Computing, 13(5):501-516, 2002. URL: https://doi.org/10.1006/jvlc.2002.0232.
  37. Helen C. Purchase, Robert F. Cohen, and Murray James. Validating graph drawing aesthetics. In Graph Drawing, pages 435-446. Springer, 1996. URL: https://doi.org/10.1007/BFb0021827.
  38. Roberto Tamassia. Handbook of graph drawing and visualization. CRC press, 2013. Google Scholar
  39. Matteo Tiezzi, Gabriele Ciravegna, and Marco Gori. Graph neural networks for graph drawing. IEEE Transactions on Neural Networks and Learning Systems, 35(4):4668-4681, 2022. URL: https://doi.org/10.1109/TNNLS.2022.3184967.
  40. Warren S. Torgerson. Multidimensional scaling: I. Theory and method. Psychometrika, 17(4):401-419, 1952. URL: https://doi.org/10.1007/BF02288916.
  41. Laurens van der Maaten. Accelerating t-SNE using tree-based algorithms. The Journal of Machine Learning Research, 15(1):3221-3245, 2014. URL: https://doi.org/10.5555/2627435.2697068.
  42. Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2008. Google Scholar
  43. Xiaoqi Wang, Kevin Yen, Yifan Hu, and Han-Wei Shen. DeepGD: A deep learning framework for graph drawing using GNN. IEEE CGA, 41(5):32-44, 2021. URL: https://doi.org/10.1109/MCG.2021.3093908.
  44. Xiaoqi Wang, Kevin Yen, Yifan Hu, and Han-Wei Shen. SmartGD: A gan-based graph drawing framework for diverse aesthetic goals. IEEE TVCG, 30(8):5666-5678, 2023. URL: https://doi.org/10.1109/TVCG.2023.3306356.
  45. Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, and Huamin Qu. DeepDrawing: A deep learning approach to graph drawing. IEEE TVCG, 26(1):676-686, 2020. URL: https://doi.org/10.1109/TVCG.2019.2934798.
  46. Jonathan X. Zheng, Samraat Pawar, and Dan F. M. Goodman. Graph drawing by stochastic gradient descent. IEEE TVCG, 25(9):2738-2748, 2019. URL: https://doi.org/10.1109/TVCG.2018.2859997.
  47. Minfeng Zhu, Wei Chen, Yuanzhe Hu, Yuxuan Hou, Liangjun Liu, and Kaiyuan Zhang. DRGraph: An efficient graph layout algorithm for large-scale graphs by dimensionality reduction. IEEE TVCG, 27(2):1666-1676, 2020. URL: https://doi.org/10.1109/TVCG.2020.3030447.
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