Discovering anomalous sequences in attributed graphs: A parameter-light approach

Graphs have been widely used across scientific disciplines, from sociology to biology, particularly when modeling temporal evolution. Although many algorithms have been developed to discover patterns in graphs, they face three main limitations. First, most algorithms assume that each node or edge is associated with a single attribute, whereas real-world applications often involve multiple attributes to capture events more comprehensively. Second, existing methods typically require tuning several hyperparameters, which can vary significantly across different datasets. Third, most approaches focus on identifying frequent patterns, often overlooking rare but meaningful ones. To address these limitations, this paper proposes a framework for discovering anomalous sequences in attributed graphs. Instead of relying on frequency-based measures, the framework adopts an entropy-based method for pattern mining, thereby requiring at most one hyperparameter. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach in detecting anomalous sequences. Moreover, we extend the framework to applications in optics, where it is used to evaluate phase differences.