IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v387y2008i24p6190-6200.html

Analyzing open-source software systems as complex networks

Author

Listed:
  • Zheng, Xiaolong
  • Zeng, Daniel
  • Li, Huiqian
  • Wang, Feiyue

Abstract

Software systems represent one of the most complex man-made artifacts. Understanding the structure of software systems can provide useful insights into software engineering efforts and can potentially help the development of complex system models applicable to other domains. In this paper, we analyze one of the most popular open-source Linux meta packages/distributions called the Gentoo Linux. In our analysis, we model software packages as nodes and dependencies among them as edges. Our empirical results show that the resulting Gentoo network cannot be easily explained by existing complex network models. This in turn motivates our research in developing two new network growth models in which a new node is connected to an old node with the probability that depends not only on the degree but also on the “age” of the old node. Through computational and empirical studies, we demonstrate that our models have better explanatory power than the existing ones. In an effort to further explore the properties of these new models, we also present some related analytical results.

Suggested Citation

  • Zheng, Xiaolong & Zeng, Daniel & Li, Huiqian & Wang, Feiyue, 2008. "Analyzing open-source software systems as complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6190-6200.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:24:p:6190-6200
    DOI: 10.1016/j.physa.2008.06.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108005852
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2008.06.050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Jianguo & Dang, Yanzhong & Wang, Zhongtuo & Zhou, Tao, 2006. "Relationship between the in-degree and out-degree of WWW," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 861-869.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    3. von Krogh, Georg & Spaeth, Sebastian & Lakhani, Karim R., 2003. "Community, joining, and specialization in open source software innovation: a case study," Research Policy, Elsevier, vol. 32(7), pages 1217-1241, July.
    4. Ergün, G. & Rodgers, G.J., 2002. "Growing random networks with fitness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 303(1), pages 261-272.
    5. Barabási, Albert-László & Ravasz, Erzsébet & Vicsek, Tamás, 2001. "Deterministic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(3), pages 559-564.
    6. Colizza, Vittoria & Flammini, Alessandro & Maritan, Amos & Vespignani, Alessandro, 2005. "Characterization and modeling of protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(1), pages 1-27.
    7. Jayanth R. Banavar & Amos Maritan & Andrea Rinaldo, 1999. "Size and form in efficient transportation networks," Nature, Nature, vol. 399(6732), pages 130-132, May.
    8. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    9. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rashid, Mehvish & Clarke, Paul M. & O’Connor, Rory V., 2019. "A systematic examination of knowledge loss in open source software projects," International Journal of Information Management, Elsevier, vol. 46(C), pages 104-123.
    2. Li, Hui & Zhao, Hai & Cai, Wei & Xu, Jiu-Qiang & Ai, Jun, 2013. "A modular attachment mechanism for software network evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2025-2037.
    3. Cornelius Fritz & Co-Pierre Georg & Angelo Mele & Michael Schweinberger, 2024. "Vulnerability Webs: Systemic Risk in Software Networks," Papers 2402.13375, arXiv.org, revised Jul 2025.
    4. Wang, Haoqin & Chen, Zhen & Xiao, Guanping & Zheng, Zheng, 2016. "Network of networks in Linux operating system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 520-526.
    5. Youzhong Wang & Daniel Zeng & Bin Zhu & Xiaolong Zheng & Feiyue Wang, 2014. "Patterns of news dissemination through online news media: A case study in China," Information Systems Frontiers, Springer, vol. 16(4), pages 557-570, September.
    6. James Ma & Daniel Zeng & Huimin Zhao, 2012. "Modeling the growth of complex software function dependency networks," Information Systems Frontiers, Springer, vol. 14(2), pages 301-315, April.
    7. Xiao, Guanping & Zheng, Zheng & Wang, Haoqin, 2017. "Evolution of Linux operating system network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 249-258.
    8. Šubelj, Lovro & Bajec, Marko, 2011. "Community structure of complex software systems: Analysis and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2968-2975.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Hui & Zhao, Hai & Cai, Wei & Xu, Jiu-Qiang & Ai, Jun, 2013. "A modular attachment mechanism for software network evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2025-2037.
    2. Claes Andersson & Koen Frenken & Alexander Hellervik, 2006. "A Complex Network Approach to Urban Growth," Environment and Planning A, , vol. 38(10), pages 1941-1964, October.
    3. Zhang, Zhongzhi & Rong, Lili & Comellas, Francesc, 2006. "High-dimensional random Apollonian networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 610-618.
    4. Yao, Xin & Zhang, Chang-shui & Chen, Jin-wen & Li, Yan-da, 2005. "On the formation of degree and cluster-degree correlations in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 661-673.
    5. Paul Sheridan & Yuichi Yagahara & Hidetoshi Shimodaira, 2008. "A preferential attachment model with Poisson growth for scale-free networks," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 747-761, December.
    6. Ikeda, Nobutoshi, 2019. "Growth model for fractal scale-free networks generated by a random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 424-434.
    7. Pandey, Pradumn Kumar & Adhikari, Bibhas, 2015. "Context dependent preferential attachment model for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 499-508.
    8. Wang, Difei & Jian, Lirong & Cao, Fengyuan & Xue, Chenyan, 2022. "An extended scale-free network evolution model based on star-like coupling motif embedding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    9. Dangalchev, Chavdar, 2004. "Generation models for scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 659-671.
    10. Pi, Xiaochen & Tang, Longkun & Chen, Xiangzhong, 2021. "A directed weighted scale-free network model with an adaptive evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    11. Kazemilari, Mansooreh & Mardani, Abbas & Streimikiene, Dalia & Zavadskas, Edmundas Kazimieras, 2017. "An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach," Renewable Energy, Elsevier, vol. 102(PA), pages 107-117.
    12. Ikeda, N., 2007. "Network formed by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 701-713.
    13. Çavuşoğlu, Abdullah & Türker, İlker, 2013. "Scientific collaboration network of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 9-18.
    14. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    15. He, Xuan & Zhao, Hai & Cai, Wei & Liu, Zheng & Si, Shuai-Zong, 2014. "Earthquake networks based on space–time influence domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 175-184.
    16. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    17. Dávid Csercsik & Sándor Imre, 2017. "Cooperation and coalitional stability in decentralized wireless networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 571-584, April.
    18. Rodrigo Huerta-Quintanilla & Efrain Canto-Lugo & Dolores Viga-de Alva, 2013. "Modeling Social Network Topologies in Elementary Schools," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
    19. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    20. Omar A Guerrero & Robert L Axtell, 2013. "Employment Growth through Labor Flow Networks," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-12, May.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:387:y:2008:i:24:p:6190-6200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.