  Detecting anomaly edges for dynamic graphs aims to identify edges
significantly deviating from the normal pattern and can be applied in various
domains, such as cybersecurity, financial transactions and AIOps. With the
evolving of time, the types of anomaly edges are emerging and the labeled
anomaly samples are few for each type. Current methods are either designed to
detect randomly inserted edges or require sufficient labeled data for model
training, which harms their applicability for real-world applications. In this
paper, we study this problem by cooperating with the rich knowledge encoded in
large language models(LLMs) and propose a method, namely AnomalyLLM. To align
the dynamic graph with LLMs, AnomalyLLM pre-trains a dynamic-aware encoder to
generate the representations of edges and reprograms the edges using the
prototypes of word embeddings. Along with the encoder, we design an in-context
learning framework that integrates the information of a few labeled samples to
achieve few-shot anomaly detection. Experiments on four datasets reveal that
AnomalyLLM can not only significantly improve the performance of few-shot
anomaly detection, but also achieve superior results on new anomalies without
any update of model parameters.
