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    基于子图显著性剖面的软件超家族

    Software Superfamilies Based on Sub-Graph Significance Profile

    • 摘要: 研究了开源软件的网络结构中3节点子图的显著性,发现软件规模越大局部结构的网络化趋势越显著.树型的3节点子图呈现下降趋势,在封闭的3节点子图中,除部分趋势不显著外,大都呈现上升趋势.根据3节点子图的显著性剖面,软件网络大致可以分为3类,与已发现的4个有向复杂网络超级族中的3个基本一致,大部分软件网络的局部结构与生物网络相似.网络的规模可能是影响子图显著性差异的原因之一,随着软件规模的增加,3节点子图的显著性趋于一致.

       

      Abstract: The significance of triad appeared in open source software is studied. It is found that the local structure trends to be networked with the increase of software scale. Tree style sub-graph trends to decrease, but most of close style sub-graph trends to increase. After comparing triad significance profiles and correlation between open source software networks, we have found that software networks could be divided into 3 clusters which are consistent with 3 of 4 well known super-families contain different networks from various domains. Most software networks have similar local structure with biological networks. It seems that software scale may be one of the reasons causing different sub-graph significance profiles. With the increase of software scale, the significance profile of triad trends to be similar. The experiment results show that the software network topology is very similar to biological networks, part of the research methods, research results and evolution mechanism of biological network are very helpful to the research in software network. The experimental results also show analyzing software networks, in particular to analyze the small-scale software network, that should be the first to analyze the local structural features. Depending on the local characteristics, it is necessary to take a different research approach and research strategy, not simply be treat them equally.

       

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