Stock correlation network, a su bset of financial network, is built on the stock price correlation. It is used for observing, analyzing and predicting the stock market dynamics. Existing correlation methods include the minimum spanning tree (MST), planar maximally filtered graph (PMFG), and winner take all (WTA). The MST and PMFG methods lose information due to the connection criterion and thereby fail to include certain highly correlated stocks. The WTA method, when used for a nonlinear system such as stock prices, fails to capture the dynamic behavior embedded in the time series oft he stocks. In this paper we present a new m ethod, which we call phase synchronization (PS) for constructing and analyzing the stock correlation network.The PS method captures the dynamic behavior of the time series of stocks and mitigates the information loss. To test the proposed PS method we use the weekly closing stock prices of the S& index (439 stocks) from 20002009.The PS m ethod provides valuable insights into the behavior of highly correlated stocks which can be useful for making trading decisions. The network exhibits a scale free degree distribution for both chaotic and nonchaoticperiods.