Discovering important nodes in graph data attracted a lot of attention. Social networks are good examples of graph data in which each node represents a person and each edge represents a relationship between two people. There are several methods for the task of discovering important nodes in graph data. In this paper, important people are defined with their roles in society or organization. We propose an efficient method to discover leaders in graph network. For this purpose, both structural feature like entropy and inherent features including from, to, subject and message's time of social networks are used to propose a novel method for discovering important nodes in social networks. The proposed method was applied to Enron dataset and compared with previous methods. The proposed method succeeded to first, discover more important roles in Enron dataset, second, determine CEO as leader of Enron Corporation and third, discover two out of four CEOs among top VIPs.
