Community detection is one of the most important research topics in the complex network area. The identification of community structure in large graphs analyze the information unrevealed in the exterior data relationships, explore the internal structure and the function of networks and improve their efficacity. A lot of approaches and methods have been proposed to identify communities based on network structure. However, the majority of them focus on topologies of nodes but ignore the relevance of interactions between them. In this paper, we propose a novel algorithm especially focused on identifying the initial communities then expanding them by using a new node tightness degree based on the edge clustering coefficient and the shared neighbour's similarity of nodes. The proposed approach is evaluated based on different small and large datasets corresponding to different contexts. The experiments prove good results in terms of modularity and computation time while using the new node tightness degree