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Dependence centrality similarity: Measuring the diversity of profession levels of interests

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  • Yan, Deng-Cheng
  • Li, Ming
  • Wang, Bing-Hong

Abstract

To understand the relations between developers and software, we study a collaborative coding platform from the perspective of networks, including follower networks, dependence networks and developer-project bipartite networks. Through the analyzing of degree distribution, PageRank and degree-dependent nearest neighbors’ centrality, we find that the degree distributions of all networks have a power-law form except the out-degree distributions of dependence networks. The nearest neighbors’ centrality is negatively correlated with degree for developers but fluctuates around the average for projects. In order to measure the diversity of profession levels of interests, a new index called dependence centrality similarity is proposed and the correlation between dependence centrality similarity and degree is investigated. The result shows an obvious negative correlations between dependence centrality similarity and degree.

Suggested Citation

  • Yan, Deng-Cheng & Li, Ming & Wang, Bing-Hong, 2017. "Dependence centrality similarity: Measuring the diversity of profession levels of interests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 118-127.
    • Handle: RePEc:eee:phsmap:v:479:y:2017:i:c:p:118-127
    DOI: 10.1016/j.physa.2017.02.082
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