From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection

Yixin Liu; Shiyuan Li; Yu Zheng; Qingfeng Chen; Chengqi Zhang; Philip S. Yu; Shirui Pan

cs.LGFeb 21, 2026

From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection

Authors: Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Philip S. Yu, Shirui Pan

Organizations: School of Information and Communication Technology, Griffith University, Australia. · School of Computer, Electronics and Information, Guangxi University, Nanning, China. · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong. · Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607-7053, USA.

Abstract

Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GAD methods often focus on the learning paradigms of "one-model-for-one-dataset", requiring dataset-specific training for each dataset to achieve optimal performance. However, this paradigm suffers from significant limitations, such as high computational and data costs, limited generalization and transferability to new datasets, and challenges in privacy-sensitive scenarios where access to full datasets or sufficient labels is restricted. To address these limitations, we propose a novel generalist GAD paradigm that aims to develop a unified model capable of detecting anomalies on multiple unseen datasets without extensive retraining/fine-tuning or dataset-specific customization. To this end, we propose ARC, a few-shot generalist GAD method that leverages in-context learning and requires only a few labeled normal samples at inference time. Specifically, ARC consists of three core modules: a feature Alignment module to unify and align features across datasets, a Residual GNN encoder to capture dataset-agnostic anomaly representations, and a cross-attentive in-Context learning module to score anomalies using few-shot normal context. Building on ARC, we further introduce ARC_zero for the zero-shot generalist GAD setting, which selects representative pseudo-normal nodes via a pseudo-context mechanism and thus enables fully label-free inference on unseen datasets. Extensive experiments on 17 real-world graph datasets demonstrate that both ARC and ARC_zero effectively detect anomalies, exhibit strong generalization ability, and perform efficiently under few-shot and zero-shot settings.

Figures & tables

Fig. 1: The sketch maps of the existing data-specific GAD paradigms ((a) supervised and (b) unsupervised) and our generalist GAD paradigms ((c) few-shot and (d) zero-shot).

Fig. 2: The overall pipeline of the proposed methods ARC, illustrating by (a)➜(b)➜(c), and ARC zero , illustrating by (a)➜(b)➜(d).

Panel of Figure 3 — Fig. 3: AUROC on data with 5 groups of feature subsets.

Fig. 4: Toy examples of reconstruction-based scoring mechanism. In each case, nodes 1, 2, 3, and 4 are normal samples, while node 5 is an anomaly.

Citation network with injected anomalies
Dataset	$\mathcal{T}_{train}$	$\mathcal{T}_{test}$	#Nodes	#Edges	#Feat.	#Ano.
Cora	-	$\checkmark$	2,708	5,429	1,433	150
CiteSeer	-	$\checkmark$	3,327	4,732	3,703	150
ACM	-	$\checkmark$	16,484	71,980	8,337	597
PubMed	$\checkmark$	-	19,717	44,338	500	600
Social network with injected anomalies

TABLE I: The statistics of datasets.

Method	Cora	CiteSeer	ACM	BlogCatalog	Facebook	Weibo	Reddit
Supervised - Pre-Train Only
GCN	$59.64{\scriptstyle\pm 8.30}$	$60.27{\scriptstyle\pm 8.11}$	$60.49{\scriptstyle\pm 9.65}$	$56.19{\scriptstyle\pm 6.39}$	$29.51{\scriptstyle\pm 4.86}$	$76.64{\scriptstyle\pm 17.69}$	$50.43{\scriptstyle\pm 4.41}$
GAT	$50.06{\scriptstyle\pm 2.65}$	$51.59{\scriptstyle\pm 3.49}$	$48.79{\scriptstyle\pm 2.73}$	$50.40{\scriptstyle\pm 2.80}$	$51.88{\scriptstyle\pm 2.16}$	$53.06{\scriptstyle\pm 7.48}$	$51.78{\scriptstyle\pm 4.04}$
BGNN	$42.45{\scriptstyle\pm 11.57}$	$42.32{\scriptstyle\pm 11.82}$	$44.00{\scriptstyle\pm 13.69}$	$47.67{\scriptstyle\pm 8.52}$	$54.74{\scriptstyle\pm 25.29}$	$32.75{\scriptstyle\pm 35.35}$	$50.27{\scriptstyle\pm 3.84}$
BWGNN	$54.06{\scriptstyle\pm 3.27}$	$52.61{\scriptstyle\pm 2.88}$	$67.59{\scriptstyle\pm 0.70}$	$56.34{\scriptstyle\pm 1.21}$	$45.84{\scriptstyle\pm 4.97}$	$53.38{\scriptstyle\pm 1.61}$	$48.97{\scriptstyle\pm 5.74}$
GHRN	$59.89{\scriptstyle\pm 6.57}$	$56.04{\scriptstyle\pm 9.19}$	$55.65{\scriptstyle\pm 6.37}$	$57.64{\scriptstyle\pm 3.48}$	$44.81{\scriptstyle\pm 8.06}$	$51.87{\scriptstyle\pm 14.18}$	$46.22{\scriptstyle\pm 2.33}$

TABLE II: Anomaly detection performance in terms of AUROC (in percent, mean

\pm

std). Highlighted are the results ranked first , second , and third . “Rank” indicates the average ranking over 13 datasets. Superscript “*” on a baseline method name indicates that our method is significantly different from this baseline on ALL_DATASETS_TRIAL_AVG (paired t-test and Wilcoxon test, p

<

0.05).

Variant	Cora	CiteSeer	ACM	BlogCatalog	Facebook	Weibo	Reddit	Amazon
ARC	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{87.45{\scriptstyle\pm 0.74}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{90.95{\scriptstyle\pm 0.59}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{79.88{\scriptstyle\pm 0.28}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{74.76{\scriptstyle\pm 0.06}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{67.56{\scriptstyle\pm 1.60}}}}$	$88.85{\scriptstyle\pm 0.14}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{60.04{\scriptstyle\pm 0.69}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{80.67{\scriptstyle\pm 1.81}}}}$
w/o A	$80.65{\scriptstyle\pm 0.71}$	$83.35{\scriptstyle\pm 0.64}$	$79.29{\scriptstyle\pm 0.16}$	$73.86{\scriptstyle\pm 0.18}$	$62.80{\scriptstyle\pm 2.06}$	$89.69{\scriptstyle\pm 0.17}$	$54.60{\scriptstyle\pm 1.92}$	$64.76{\scriptstyle\pm 2.13}$
w/o R	$37.44{\scriptstyle\pm 1.40}$	$31.52{\scriptstyle\pm 0.71}$	$61.83{\scriptstyle\pm 1.16}$	$49.30{\scriptstyle\pm 2.06}$	$20.38{\scriptstyle\pm 9.63}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{97.72{\scriptstyle\pm 0.59}}}}$	$52.94{\scriptstyle\pm 0.96}$	$50.15{\scriptstyle\pm 0.24}$
w/o C	$47.39{\scriptstyle\pm 0.42}$	$53.98{\scriptstyle\pm 0.72}$	$54.24{\scriptstyle\pm 1.32}$	$60.46{\scriptstyle\pm 1.23}$	$48.86{\scriptstyle\pm 0.97}$	$42.84{\scriptstyle\pm 3.01}$	$51.03{\scriptstyle\pm 0.86}$	$69.02{\scriptstyle\pm 0.97}$

TABLE III: Performance of ARC and its variants in terms of AUROC.

Variant	Cora	CiteSeer	ACM	BlogCatalog	Facebook	Weibo	Reddit	Amazon
ARC zero	$87.54{\scriptstyle\pm 0.57}$	$91.20{\scriptstyle\pm 0.27}$	$78.78{\scriptstyle\pm 0.17}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{74.75{\scriptstyle\pm 0.03}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{69.25{\scriptstyle\pm 0.95}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{88.71{\scriptstyle\pm 0.18}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{59.57{\scriptstyle\pm 0.42}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{83.76{\scriptstyle\pm 1.01}}}}$
w/o Iter. Score	$87.76{\scriptstyle\pm 0.60}$	$90.13{\scriptstyle\pm 0.39}$	$79.23{\scriptstyle\pm 0.13}$	$74.71{\scriptstyle\pm 0.15}$	$67.78{\scriptstyle\pm 1.00}$	$88.17{\scriptstyle\pm 0.13}$	$59.13{\scriptstyle\pm 0.53}$	$80.85{\scriptstyle\pm 1.64}$
w/o Multi-Round	$87.37{\scriptstyle\pm 0.55}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{91.48{\scriptstyle\pm 0.26}}}}$	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{79.70{\scriptstyle\pm 0.09}}}}$	$74.47{\scriptstyle\pm 0.04}$	$65.52{\scriptstyle\pm 0.93}$	$88.25{\scriptstyle\pm 0.13}$	$58.16{\scriptstyle\pm 0.74}$	$80.92{\scriptstyle\pm 1.09}$
w Random Init	$86.47{\scriptstyle\pm 0.17}$	$90.10{\scriptstyle\pm 0.21}$	$79.69{\scriptstyle\pm 0.04}$	$74.15{\scriptstyle\pm 0.13}$	$65.40{\scriptstyle\pm 1.12}$	$79.36{\scriptstyle\pm 4.32}$	$58.92{\scriptstyle\pm 0.33}$	$82.91{\scriptstyle\pm 0.27}$
w Degree Init	$87.30{\scriptstyle\pm 0.52}$	$91.31{\scriptstyle\pm 0.23}$	$79.56{\scriptstyle\pm 0.09}$	$73.92{\scriptstyle\pm 0.17}$	$68.46{\scriptstyle\pm 1.62}$	$84.28{\scriptstyle\pm 2.18}$	$59.21{\scriptstyle\pm 0.32}$	$78.82{\scriptstyle\pm 2.53}$
w Rep. Clu. Init	${\color[rgb]{0.0781,0.5,0.332}\underline{\mathbf{87.87{\scriptstyle\pm 0.53}}}}$	$91.45{\scriptstyle\pm 0.23}$	$74.51{\scriptstyle\pm 0.95}$	$69.59{\scriptstyle\pm 1.18}$	$68.39{\scriptstyle\pm 1.09}$	$60.42{\scriptstyle\pm 13.45}$	$52.57{\scriptstyle\pm 1.31}$	$72.26{\scriptstyle\pm 1.70}$

TABLE IV: Performance of ARC zero and its variants in terms of AUROC.

Panel of Figure 9 — Fig. 5: Performance with varying $n_{k}$ in few-shot method ARC. The shaded areas represent the standard deviation over 5 repeated runs.

Panel of Figure 10 — Fig. 6: Performance with varying $n_{k}$ in zero-shot method ARC zero . The shaded areas represent the standard deviation over 5 repeated runs.

Fig. 7: Efficiency comparison of inference and fine-tuning time.

Panel of Figure 12 — Fig. 8: Visualization of the attention weights of cross-attentive scoring module. Darker colors represent larger values.

Panel of Figure 13 — Fig. 9: t-SNE visualization of representations by ARC and baselines.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 14 — Fig. 10: Hyperparameter sensitivity analysis.

Supervised - Pre-Train Only
Method	Cora	CiteSeer	ACM	BlogCatalog	Facebook	Weibo	Reddit
GCN	$7.41{\scriptstyle\pm 1.55}$	$6.40{\scriptstyle\pm 1.40}$	$5.27{\scriptstyle\pm 1.12}$	$7.44{\scriptstyle\pm 1.07}$	$1.59{\scriptstyle\pm 0.11}$	${\color[rgb]{0.9258,0.3281,0.0781}\underline{67.21{\scriptstyle\pm 15.20}}}$	$3.39{\scriptstyle\pm 0.39}$
GAT	$6.49{\scriptstyle\pm 0.84}$	$5.58{\scriptstyle\pm 0.62}$	$4.70{\scriptstyle\pm 0.75}$	$12.81{\scriptstyle\pm 2.08}$	$3.14{\scriptstyle\pm 0.37}$	$33.34{\scriptstyle\pm 9.80}$	$3.73{\scriptstyle\pm 0.54}$
BGNN	$4.90{\scriptstyle\pm 1.27}$	$3.91{\scriptstyle\pm 1.01}$	$3.48{\scriptstyle\pm 1.33}$	$5.73{\scriptstyle\pm 1.47}$	$3.81{\scriptstyle\pm 2.12}$	$30.26{\scriptstyle\pm 29.98}$	$3.52{\scriptstyle\pm 0.50}$
BWGNN	$7.25{\scriptstyle\pm 0.80}$	$6.35{\scriptstyle\pm 0.73}$	$7.14{\scriptstyle\pm 0.20}$	$8.99{\scriptstyle\pm 1.12}$	$2.54{\scriptstyle\pm 0.63}$	$12.13{\scriptstyle\pm 0.71}$	$3.69{\scriptstyle\pm 0.81}$
GHRN	$9.56{\scriptstyle\pm 2.40}$	$7.79{\scriptstyle\pm 2.01}$	$5.61{\scriptstyle\pm 0.71}$	$10.94{\scriptstyle\pm 2.56}$	$2.41{\scriptstyle\pm 0.62}$	$28.53{\scriptstyle\pm 7.38}$	$3.24{\scriptstyle\pm 0.33}$

Appendix

TABLE V: Anomaly detection performance in terms of AUPRC (in percent, mean

\pm

<

0.05).

Panel of Figure 16 — Fig. 11: Additional results for varying $n_{k}$ in the few-shot setting (ARC).

Panel of Figure 17 — Fig. 12: Additional results for varying $n_{k}$ in the zero-shot setting (ARC zero ).

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Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.

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