Coupled behaviors, which refer to behaviors having some relationships between them, are usually seen in many real-world scenarios, especially in stock markets. Recently, the coupled hidden Markov model (CHMM)-based coupled behavior analysis has been proposed to consider the coupled relationships in a hidden state space. However, it requires aggregation of the behavioral data to cater for the CHMM modeling, which may overlook the couplings within the aggregated behaviors to some extent. In addition, the Markov assumption limits its capability to capturing temporal couplings. Thus, this paper proposes a novel graph-based framework for detecting abnormal coupled behaviors. The proposed framework represents the coupled behaviors in a graph view without aggregating the behavioral data and is flexible to capture richer coupling information of the behaviors (not necessarily temporal relations). On top of that, the couplings are learned via relational learning methods and an efficient anomaly detection algorithm is proposed as well. Experimental results on a real-world data set in stock markets show that the proposed framework outperforms the CHMM-based one in both technical and business measures.