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Co-Occurrence Network Of Reuters News

Author

Listed:
  • ARZUCAN ÖZGÜR

    (Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA)

  • BURAK CETIN

    (Complex Systems Research Laboratory, Department of Computer Engineering, Bogazici University, Istanbul, Turkey)

  • HALUK BINGOL

    (Complex Systems Research Laboratory, Department of Computer Engineering, Bogazici University, Istanbul, Turkey)

Abstract

Networks describe various complex natural systems including social systems. We investigate the social network of co-occurrence in Reuters-21578 corpus, which consists of news articles that appeared in the Reuters newswire in 1987. People are represented as vertices and two persons are connected if they co-occur in the same article. The network has small-world features with power-law degree distribution. The network is disconnected and the component size distribution has power-law characteristics. Community detection on a degree-reduced network provides meaningful communities. An edge-reduced network, which contains only the strong ties has a star topology. "Importance" of persons are investigated. The network is the situation in 1987. After 20 years, a better judgment on the importance of the people can be done. A number of ranking algorithms, including Citation count and PageRank, are used to assign ranks to vertices. The ranks given by the algorithms are compared against how well a person is represented in Wikipedia. We find up to medium level Spearman's rank correlations. A noteworthy finding is that PageRank consistently performed worse than the other algorithms. We analyze this further and find reasons.

Suggested Citation

  • Arzucan Özgür & Burak Cetin & Haluk Bingol, 2008. "Co-Occurrence Network Of Reuters News," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 689-702.
    • Handle: RePEc:wsi:ijmpcx:v:19:y:2008:i:05:n:s0129183108012431
    DOI: 10.1142/S0129183108012431
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    Cited by:

    1. Sarlin, Peter & Rönnqvist, Samuel, 2016. "Bank networks from text: interrelations, centrality and determinants," Working Paper Series 1876, European Central Bank.
    2. Samuel Ronnqvist & Peter Sarlin, 2014. "Bank Networks from Text: Interrelations, Centrality and Determinants," Papers 1406.7752, arXiv.org, revised Jul 2015.
    3. Ksenia Silchenko & Roberto Del Gobbo & Nicola Castellano & Bruno Maria Franceschetti & Virginia Tosi & Monia La Verghetta, 2015. "Sorting Through Waste Management Literature: A Text Mining Approach to a Literature Review," MIC 2015: Managing Sustainable Growth; Proceedings of the Joint International Conference, Portorož, Slovenia, 28–30 May 2015,, University of Primorska, Faculty of Management Koper.

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