  Finding the important nodes in complex networks by topological structure is
of great significance to network invulnerability. Several centrality measures
have been proposed recently to evaluate the performance of nodes based on their
correlation, showing that the interaction between nodes has an influence on the
importance of nodes. In this paper, a novel method based on node distribution
and global influence in complex networks is proposed. Our main idea is that the
importance of nodes being linked not only to the relative position in the
network but also to the correlations with each other. The nodes in the complex
networks are classified according to the distance matrix, then the correlation
coefficient between pairs of nodes is calculated. From the whole perspective in
the network, the global similarity centrality (GSC) is proposed based on the
relevance and shortest distance between any two nodes. The efficiency, accuracy
and monotonicity of the proposed method are analyzed in two artificial datasets
and eight real datasets of different sizes. Experimental results show that the
performance of GSC method outperforms those current state-of-the-art
algorithms.
