In this work, we propose an unsupervised approach for crowd scene anomaly detection and localization using a social network model. Using a window-based approach, a video scene is first partitioned at spatial and temporal levels, and a set of spatio-temporal cuboids is constructed. Objects exhibiting scene dynamics are detected and the crowd behavior in each cuboid is modeled using local social networks (LSN). From these local social networks, a global social network (GSN) is built for the current window to represent the global behavior of the scene. As the scene evolves with time, the global social network is updated accordingly using LSNs, to detect and localize abnormal behaviors. We demonstrate the effectiveness of the proposed Social Network Model (SNM) approach on a set of benchmark crowd analysis video sequences. The experimental results reveal that the proposed method outperforms the majority, if not all, of the state-of-the-art methods in terms of accuracy of anomaly detection.