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Scalable force-directed graph representation learning and visualization

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Abstract

A graph embedding algorithm embeds a graph into a low-dimensional space such that the embedding preserves the inherent properties of the graph. While graph embedding is fundamentally related to graph visualization, prior work did not exploit this connection explicitly. We develop Force2Vec that uses force-directed graph layout models in a graph embedding setting with an aim to excel in both machine learning (ML) and visualization tasks. We make Force2Vec highly parallel by mapping its core computations to linear algebra and utilizing multiple levels of parallelism available in modern processors. The resultant algorithm is an order of magnitude faster than existing methods (43\(\times \) faster than DeepWalk, on average) and can generate embeddings from graphs with billions of edges in a few hours. In comparison to existing methods, Force2Vec is better in graph visualization and performs comparably or better in ML tasks such as link prediction, node classification, and clustering. Source code is available at https://github.com/HipGraph/Force2Vec.This paper is an extension of a conference paper by Rahman et al. (in: 20th IEEE international conference on data mining, IEEE ICDM, 2020b) published in IEEE ICDM 2020.

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References

  1. Ahmed A, Shervashidze N, Narayanamurthy S, Josifovski V, Smola AJ (2013) Distributed large-scale natural graph factorization. In: Proceedings of WWW, pp 37–48

  2. Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: spotting anomalies in weighted graphs. In: Proceedings of PAKDD. Springer, pp 410–421

  3. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:P10008

    Article  Google Scholar 

  4. Bolosky WJ, Scott ML (1993) False sharing and its effect on shared memory performance. In: 4th symposium on experimental distributed and multiprocessor systems, pp 57–71

  5. Brandes U, Pich C (2008) An experimental study on distance-based graph drawing. In: International symposium on graph drawing. Springer, pp 218–229

  6. Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637

    Article  Google Scholar 

  7. Cao S, Lu W, Xu Q (2015) GraRep: learning graph representations with global structural information. In: Proceedings of CIKM, pp 891–900

  8. Chen H, Perozzi B, Hu Y, Skiena S (2018) HARP: hierarchical representation learning for networks. In: Proceedings of AAAI

  9. De Luca F, Hossain I, Kobourov S, Börner K (2019) Multi-level tree based approach for interactive graph visualization with semantic zoom, arXiv preprint arXiv:1906.05996

  10. Erdös P, Harary F, Tutte WT (1965) On the dimension of a graph. Mathematika 12(2):118–122

    Article  MathSciNet  Google Scholar 

  11. Fruchterman TM, Reingold EM (1991) Graph drawing by force-directed placement, Softw Pract Exp 21(11):1129–1164

  12. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of KDD. ACM, pp 855–864

  13. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of NIPS, pp 1024–1034

  14. Henderson K, Gallagher B, Eliassi-Rad T, Tong H, Basu S, Akoglu L, Koutra D, Faloutsos C Li L (2012) RolX: structural role extraction & mining in large graphs. In: Proceedings of KDD, pp 1231–1239

  15. Jacomy M, Venturini T, Heymann S, Bastian M (2014) ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PloS One 9(6)

  16. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv:1609.02907

  17. Lee JA, Verleysen M (2010) Scale-independent quality criteria for dimensionality reduction. Pattern Recognit Lett 31(14):2248–2257

    Article  Google Scholar 

  18. Luo D, Nie F, Huang H, Ding CH (2011) Cauchy graph embedding. In: Proceedings of ICML, pp 553–560

  19. Maaten Lvd, Hinton G (2008) Visualizing data using t-SNE, J Mach Learn Res 9(Nov):2579–2605

  20. Martin S, Brown WM, Klavans R, Boyack KW (2011) Openord: an open-source toolbox for large graph layout. In: Visualization and data analysis 2011, Vol 7868, International society for optics and photonics, p 786806

  21. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS, pp 3111–3119

  22. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web., Technical report, Stanford InfoLab

  23. Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of KDD, pp 701–710

  24. Rahman MK, Sujon MH, Azad A (2020a) BatchLayout: a batch-parallel force-directed graph layout algorithm in shared memory. In: Proceedings of PacificVis. IEEE, pp 16–25

  25. Rahman MK, Sujon MH, Azad A (2020b), Force2Vec: parallel force-directed graph embedding. In: 20th IEEE international conference on data mining (IEEE ICDM)

  26. Recht B, Re C, Wright S, Niu F (2011) Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Proceedings of NIPS, pp 693–701

  27. Ribeiro LF, Saverese PH, Figueiredo DR (2017) struc2vec: learning node representations from structural identity. In: Proceedings of KDD, pp 385–394

  28. Tang J, Liu J, Zhang M, Mei Q (2016) Visualizing large-scale and high-dimensional data. In: Proceedings of WWW, pp 287–297

  29. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) LINE: large-scale information network embedding. In: Proceedings of WWW, pp 1067–1077

  30. Tsitsulin A, Mottin D, Karras P, Müller E (2018) Verse: versatile graph embeddings from similarity measures. In: Proceedings of WWW, pp 539–548

  31. Tutte WT (1963) How to draw a graph. Proc Lond Math Soc 3(1):743–767

    Article  MathSciNet  Google Scholar 

  32. Walshaw C (2006) A multilevel algorithm for force-directed graph-drawing. J Graph Algorithms Appl 7(3):253–285

    Article  MathSciNet  Google Scholar 

  33. Whaley RC, Petitet A, Dongarra JJ (2001) Automated empirical optimization of software and the ATLAS project. Parallel Comput 27(1–2):3–35

    Article  Google Scholar 

  34. Zeng H, Zhou H, Srivastava A, Kannan R, Prasanna V (2019) GraphSAINT: graph sampling based inductive learning method, arXiv preprint arXiv:1907.04931

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Acknowledgements

We would like to thank anonymous reviewers for their feedback. Funding for this work was provided by the Indiana University Grand Challenge Precision Health Initiative.

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  1. Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA

    Md. Khaledur Rahman, Majedul Haque Sujon & Ariful Azad

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  1. Md. Khaledur Rahman
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  2. Majedul Haque Sujon
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  3. Ariful Azad
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Correspondence to Ariful Azad.

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Rahman, M.K., Sujon, M.H. & Azad, A. Scalable force-directed graph representation learning and visualization. Knowl Inf Syst 64, 207–233 (2022). https://doi.org/10.1007/s10115-021-01634-9

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  • DOI: https://doi.org/10.1007/s10115-021-01634-9

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