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Investigating relationships within and between category networks in Wikipedia

Author

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  • Silva, F.N.
  • Viana, M.P.
  • Travençolo, B.A.N.
  • Costa, L. da F.

Abstract

This work maps and analyses cross-citations in the areas of Biology, Mathematics, Physics and Medicine in the English version of Wikipedia, which are represented as an undirected complex network where the entries correspond to nodes and the citations among the entries are mapped as edges. We found a high value of clustering coefficient for the areas of Biology and Medicine, and a small value for Mathematics and Physics. The topological organization is also different for each network, including a modular structure for Biology and Medicine, a sparse structure for Mathematics and a dense core for Physics. The networks have degree distributions that can be approximated by a power-law with a cut-off. The assortativity of the isolated networks has also been investigated and the results indicate distinct patterns for each subject. We estimated the betweenness centrality of each node considering the full Wikipedia network, which contains the nodes of the four subjects and the edges between them. In addition, the average shortest path length between the subjects revealed a close relationship between the subjects of Biology and Physics, and also between Medicine and Physics. Our results indicate that the analysis of the full Wikipedia network cannot predict the behavior of the isolated categories since their properties can be very different from those observed in the full network.

Suggested Citation

  • Silva, F.N. & Viana, M.P. & Travençolo, B.A.N. & Costa, L. da F., 2011. "Investigating relationships within and between category networks in Wikipedia," Journal of Informetrics, Elsevier, vol. 5(3), pages 431-438.
    • Handle: RePEc:eee:infome:v:5:y:2011:i:3:p:431-438
    DOI: 10.1016/j.joi.2011.03.003
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    References listed on IDEAS

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    Cited by:

    1. Silva, F.N. & Rodrigues, F.A. & Oliveira, O.N. & da F. Costa, L., 2013. "Quantifying the interdisciplinarity of scientific journals and fields," Journal of Informetrics, Elsevier, vol. 7(2), pages 469-477.
    2. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    3. Nicolas Jullien, 2012. "What We Know About Wikipedia: A Review of the Literature Analyzing the Project(s)," Post-Print hal-00857208, HAL.
    4. Amancio, Diego R. & Oliveira Jr., Osvaldo N. & Costa, Luciano da F., 2012. "Structure–semantics interplay in complex networks and its effects on the predictability of similarity in texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4406-4419.
    5. Cui, Xue-Mei & Yoon, Chang No & Youn, Hyejin & Lee, Sang Hoon & Jung, Jean S. & Han, Seung Kee, 2017. "Dynamic burstiness of word-occurrence and network modularity in textbook systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 103-110.
    6. Torres-Salinas, Daniel & Romero-Frías, Esteban & Arroyo-Machado, Wenceslao, 2019. "Mapping the backbone of the Humanities through the eyes of Wikipedia," Journal of Informetrics, Elsevier, vol. 13(3), pages 793-803.

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