Advertisement

SimpleHypergraphs.jl—Novel Software Framework for Modelling and Analysis of Hypergraphs

  • Alessia Antelmi
  • Gennaro Cordasco
  • Bogumił Kamiński
  • Paweł Prałat
  • Vittorio Scarano
  • Carmine Spagnuolo
  • Przemyslaw SzufelEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11631)

Abstract

Hypergraphs are natural generalization of graphs in which a single (hyper)edge can connect any number of vertices. As a result, hypergraphs are suitable and useful to model many important networks and processes. Typical applications are related to social data analysis and include situations such as exchanging emails with several recipients, reviewing products on social platforms, or analyzing security vulnerabilities of information networks. In many situations, using hypergraphs instead of classical graphs allows us to better capture and analyze dependencies within the network. In this paper, we propose a new library, named SimpleHypergraphs.jl, designed for efficient hypegraph analysis. The library exploits the Julia language flexibility and direct support for distributed computing in order to bring a new quality for simulating and analyzing processes represented as hypergraphs. In order to show how the library can be used we study two case studies based on the Yelp dataset. Results are promising and confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

Keywords

Hypergraphs Modelling hypergraphs Software library Julia programming language 

References

  1. 1.
  2. 2.
  3. 3.
    HyperGaph, Chapel (2019). https://github.com/pnnl/chgl (2019)
  4. 4.
    HyperGraphLib, C++ (2019). https://github.com/alex-87/HyperGraphLib
  5. 5.
    HyperNetX, Python (2019). https://github.com/pnnl/HyperNetX
  6. 6.
  7. 7.
    IPER, JavaScript (2019). https://github.com/fibo/iper
  8. 8.
    LightGraphs.jl, Julia (2019). https://github.com/JuliaGraphs/LightGraphs.jl
  9. 9.
    Multihypergraph, Python (2019). https://github.com/vaibhavkarve/multihypergraph
  10. 10.
  11. 11.
    PyGraph, Python (2019). https://github.com/jciskey/pygraph
  12. 12.
  13. 13.
  14. 14.
  15. 15.
    Antelmi, A., Cordasco, G., Spagnuolo, C., Vicidomini, L.: On evaluating graph partitioning algorithms for distributed agent based models on networks. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 367–378. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27308-2_30CrossRefGoogle Scholar
  16. 16.
    Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B.: Julia: a fresh approach to numerical computing. SIAM Rev. 59(1), 65–98 (2017)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Bretto, A.: Hypergraph Theory: An Introduction. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-00080-0CrossRefzbMATHGoogle Scholar
  18. 18.
    Cordasco, G., Spagnuolo, C., Scarano, V.: Toward the new version of D-MASON: efficiency, effectiveness and correctness in parallel and distributed agent-based simulations. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1803–1812 (2016)Google Scholar
  19. 19.
    Cordasco, G., De Chiara, R., Raia, F., Scarano, V., Spagnuolo, C., Vicidomini, L.: Designing computational steering facilities for distributed agent based simulations. In: Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pp. 385–390 (2013)Google Scholar
  20. 20.
    Danon, L., Díaz-guilera, A., Duch, J.: Comparing community structure identification. J. Stat. Mech. Theory Exp. (2005)Google Scholar
  21. 21.
    Edelman, A.: Julia: a fresh approach to technical computing and data processing. Technical report, Massachusetts Institute of Technology, Cambridge (2019)Google Scholar
  22. 22.
    Gulati, A., Eirinaki, M.: Influence propagation for social graph-based recommendations. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 2180–2189 (2018)Google Scholar
  23. 23.
    Ji, Z., Pi, H., Wei, W., Xiong, B., Woźniak, M., Damasevicius, R.: Recommendation based on review texts and social communities: a hybrid model. IEEE Access 7, 40416–40427 (2019)CrossRefGoogle Scholar
  24. 24.
    Kaminski, B., Poulin, V., Pralat, P., Szufel, P., Theberge, F.: Clustering via hypergraph modularity. arXiv preprint arXiv:1810.04816 (2018)
  25. 25.
    Li, R., Jiang, J.Y., Ju, C.J.T., Wang, W.: CORALS: who are my potential new customers? Tapping into the wisdom of customers’ decisions. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, pp. 69–77 (2019)Google Scholar
  26. 26.
    Lu, X., Qu, J., Jiang, Y., Zhao, Y.: Should i invest it?: predicting future success of yelp restaurants. In: Proceedings of the Practice and Experience on Advanced Research Computing, PEARC 2018, pp. 64:1–64:6 (2018)Google Scholar
  27. 27.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  28. 28.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 76 (2007)Google Scholar
  29. 29.
    Regier, J., et al.: Cataloging the visible universe through Bayesian inference in Julia at Petascale. J. Parallel Distrib. Comput. (2019)Google Scholar
  30. 30.
    Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alessia Antelmi
    • 1
  • Gennaro Cordasco
    • 2
  • Bogumił Kamiński
    • 3
  • Paweł Prałat
    • 4
  • Vittorio Scarano
    • 1
  • Carmine Spagnuolo
    • 1
  • Przemyslaw Szufel
    • 3
    Email author
  1. 1.Dipartimento di Informatica, Università degli Studi di SalernoFiscianoItaly
  2. 2.Dipartimento di Psicologia, Università degli Studi della Campania “Luigi Vanvitelli”CasertaItaly
  3. 3.SGH Warsaw School of EconomicsWarsawPoland
  4. 4.Department of Mathematics, Ryerson UniversityTorontoCanada

Personalised recommendations