Detecting anomalous crowd behavioral patterns from videos is an important task in video surveillance and maintaining public safety. In this work, we propose a novel architecture to detect anomalous patterns of crowd movements via graph networks. We represent individuals as nodes and individual movements with respect to other people as the node-edge relationship of an evolving graph network. We then extract the motion information of individuals using optical flow between video frames and represent their motion patterns using graph edge weights. In particular, we detect the anomalies in crowded videos by modeling pedestrian movements as graphs and then by identifying the network bottlenecks through a max-flow/min-cut pedestrian flow optimization scheme (MFMCPOS). The experiment demonstrates that the proposed framework achieves superior detection performance compared to other recently published state-of-the-art methods. Considering that our proposed approach has relatively low computational complexity and can be used in real-time environments, which is crucial for present day video analytics for automated surveillance.