Abstract: Social networks are developed rapidly and used widely in the fields such as science and technology, business, economic and biological fields. People often use the centrality to quantify the importance degree of nodes in a social network. However, in the existing centrality algorithms, researchers only use a single centrality measuring, without considering the co-effects of different measuring. Therefore, a weighted centrality is proposed which is a function of different centrality measuring. Experiments here use a real social network database BoardEX, which is provided by our cooperative research institution, the University of Hong Kong. The size of the database is about 600G. This inspires us to use the Apache Spark platform to calculate such a big data. The experimental social network is divided into four regions:the U.S.A, the United Kingdom, Europe, others. First, the degree centrality of some persons, e.g. the chief technology officers or the chief information officers in a quoted company, in each region, is calculated. Then, a weighted function is constructed to calculate the average centrality. Experimental results show that, by setting the weighted values, the difference between the weighted centrality of regions is minimized. Besides, the weighted values reflect the contributions of various centrality measuring to the weighted centrality. With the application of real social network database and big data cluster computing, a more practical and promising application prospect is showed.