我们从局部和全局网络属性的相关性中学到了什么？
What do we learn from correlations of local and global network properties?
摘要：
在复杂网络中，一个常见的任务是识别最重要或“中心”节点。有几种定义，通常称为中心性措施，这往往会导致不同的结果。在这里，我们广泛研究了四个局部和全局度量之间的相关性，即度，最短路径介数，随机游走介数和子图中心性，在不同的随机网络模型上，如Erdos-Renyi，Small-World和Barabasi-Albert，以及在不同的真实网络上，如代谢途径，社会合作和计算机网络。真实网络和模型网络之间的相关性是完全不同的，这就质疑了模型是否真的反映了现实世界的所有重要属性。
英文摘要：
In complex networks a common task is to identify the most important or "central" nodes. There are several definitions, often called centrality measures, which often lead to different results. Here we study extensively correlations between four local and global measures namely the degree, the shortest-path-betweenness, the random-walk betweenness and the subgraph centrality on different random-network models like Erdos-Renyi, Small-World and Barabasi-Albert as well as on different real networks like metabolic pathways, social collaborations and computer networks. Correlations are quite different between the real networks and the model networks questioning whether the models really reflect all important properties of the real world.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
