  Graph anomaly detection (GAD), which aims to identify abnormal nodes that
differ from the majority within a graph, has garnered significant attention.
However, current GAD methods necessitate training specific to each dataset,
resulting in high training costs, substantial data requirements, and limited
generalizability when being applied to new datasets and domains. To address
these limitations, this paper proposes ARC, a generalist GAD approach that
enables a ``one-for-all'' GAD model to detect anomalies across various graph
datasets on-the-fly. Equipped with in-context learning, ARC can directly
extract dataset-specific patterns from the target dataset using few-shot normal
samples at the inference stage, without the need for retraining or fine-tuning
on the target dataset. ARC comprises three components that are well-crafted for
capturing universal graph anomaly patterns: 1) smoothness-based feature
Alignment module that unifies the features of different datasets into a common
and anomaly-sensitive space; 2) ego-neighbor Residual graph encoder that learns
abnormality-related node embeddings; and 3) cross-attentive in-Context anomaly
scoring module that predicts node abnormality by leveraging few-shot normal
samples. Extensive experiments on multiple benchmark datasets from various
domains demonstrate the superior anomaly detection performance, efficiency, and
generalizability of ARC.
