Complex social network analysis methods have been applied extensively in various domains including online social media, biological complex networks, etc. Complex social networks are facing the challenge of information overload. The demands for efficient complex network analysis methods have been rising in recent years, particularly the extensive use of online social applications, such as Flickr, Facebook and LinkedIn. This paper aims to simplify the network complexity through partitioning a large complex network into a set of less complex networks. Existing social network analysis methods are mainly based on complex network theory and data mining techniques. These methods are facing the challenges while dealing with extreme large social network data sets. Particularly, the difficulties of maintaining the statistical characteristics of partitioned sub-networks have been increasing dramatically. The proposed Normal Distribution (ND) based method can balance the distribution of the partitioned sub-networks according to the original complex network. Therefore, each subnetwork can have its degree distribution similar to that of the original network. This can be very beneficial for analyzing sub-divided networks and potentially reducing the complexity in dynamic online social environment.