  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.
