In this paper, we analyzed topological characteristics of four famous centrality indexes (including degree, closeness, betweenness, and the k-core) and their correlations (including Pearson correlation and Kendall Rank correlation) in two real data sets. It’s the fundamental work of identifying the influential nodes in complex networks. After simulations on two real data sets, we found that the distribution of degree, betweenness, and the k-core totally follow the power-law distribution. The Pearson correlation between degree and betweenness is the highest, however, the Kendall Rank Correlation between degree and k-core is relatively larger than values between degree and other two indexes.