OwlEye: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection
Authors: Lecheng Zheng
Dongqi Fu
Zihao Li
Jingrui He
Venue: ICLR 2026 Poster (2026)
Keywords: anomaly detection
zero-shot
Multi-domain
Primary area: learning on graphs and other geometries & topologies

Abstract: Graph structured data is commonly used to represent complex relationships such as transactions between accounts, communications between devices, and dependencies among machines or processes. Correspondingly, graph anomaly detection (GAD) plays a critical role in identifying anomalies across various domains, including finance, cybersecurity, manufacturing, etc. Facing the large-volume and multi-domain graph data, recent efforts aim to develop foundational generalist models capable of detecting anomalies in unseen graphs without retraining. To the best of our knowledge, the different feature semantics and dimensions of cross-domain graph structured data heavily hinders the development of graph foundation model, and leaves the further in-depth continual learning and inference capabilities in the evolving setting a quite nascent problem. To address these above challenges, we propose OWLEYE, a novel zero-shot GAD framework that learns transferable patterns of normal behavior from multiple graphs. Systematically, OWLEYE first introduces a cross-domain feature alignment module to harmonize feature distributions, which preserves domain-specific semantics during aligning more than the simple but widely-used Principle Component Analysis. Second, with aligned features, to enable method with continuous and scaling-up learning and inference capabilities, OWLEYE designs the multi-domain pattern dictionary learning to encode shared structural and attribute-based patterns. Third, for achieving the in-context learning ability, OWLEYE presents a truncated attention-based reconstruction module to robustly detect anomalies without requiring labeled data for unseen graph structured data. Extensive experiments on real-world datasets demonstrate that OWLEYE achieves superior performance and generalizability compared to state-of-the-art baselines, establishing a strong foundation for scalable and label-efficient anomaly detection.

Published as a conference paper at ICLR 2026
OWLEYE: ZERO-SHOT LEARNER FOR CROSS- DOMAIN GRAPH DATA ANOMALY DETECTION
Lecheng Zheng', Dongqi Fu", Zihao Li", Jingrui He² Virginia Tech', Meta AI University of Illinois Urbana-Champinn lechengevt. edu,, dongqi Fuêmeta. com, {zihaoli5, (angruij@illing
ABSTRACT
Graph data is informative to represent complex relationships such as transactions between accounts, communications between devices, and dependencies among machines or processes. Correspondingly, graph anomaly detection (GAD) plays a critical role in identifying anomalies across various domains, including finance, cybersecurity, manufacturing, etc. Facing the large-volume and multi-domain graph data, nascent efforts attempt to develop foundational generalist models ca- pable of detecting anomalies in unseen graphs without retraining. To the best of our knowledge, the different feature semantics and dimensions of cross-domain graph data heavily hinder the development of the graph foundation model, leav- ing further in-depth continual learning and inference capabilities a quite open problem. Hence, we propose OWIEEY,, novel zero-shot GAD framework that learns transferable patterns of normal behavior from multiple graphs, with a three- fold contribution. First, OWLEYE proposes cross-domain feature alignment module to harmonize feature distributions which preserves domain-specific se- mantics during alignment. Second, with aligned features, to enable continuous learning capabilities, OWLEYE designs the multi-domain multi-pattern dictio- nary learning encode shared structural and attribute-based patterns. Third, for achieving the in-context learning ability, OWLEYE develops a truncated attention- reconstruction module robustly detect anomalies without re- quiring labeled data for unseen graph-structured data. Extensive experiments on real-world datasets demonstrate that OWL.EYE achieves superior performance and generalizability compared to state-of-the-an baselines, establishing strong foun- dation for scalable and label-efficient anomaly detection. The code available at OWLEYE
https:://iithu htccle//ggghhnnt..ccc////eeeeeee
INTRODUCTION
Graph anomaly detection (GAD) has been extensively studied over the past decades due to its wide- ranging applications that naturally involve graph- sraph-structured data, such as transaction networks in financial fraud detection (Slipenchuk & Epishkina 2019 Ramakrishnan et al.] 2019), communica-
tion and access networks in cybersecurity intrusion detection (Brdiczka et al. 2012 Duan et al, 2023). and user-user interaction graphs in fake news detection social networks (Shu al. 2017
2019 Fu et al. 2022a). Driven by the growing demand for accurate and scalable anomaly detection, recent research increasingly leverages graph neural networks (GNNs) model node-level irregu- larities in complex graph-structured data. Broadly, existing approaches to GAD can be categorized into two research directions. The first adopts a "one model for one dataset" paradigm (Qiao & Pang 2023 Zheng et al., 2202aa] Liu et al., 2022. [Huang et al.) [022]] Qiao et al.] 2025). where one single model trained for each graph individually to detect anomalies within that specific context. While this strategy can be effective, it is often computationally expensive and suffers from limited general- izability to unseen graphs. In contrast, a new research direction aims to build "one for all" generalist frameworks (Nuu et al. 2024 Liu et al.] 2024) that are trained on multiple graphs and capable of detecting anomalies in entirely new, unseen graphs without retraining. These models offer advan- tages in terms of scalability and cross-domain adaptability. For example, ARC (Liu al.) 2024) introduces a generalist framework based on in-context learning, which encodes high-order affinity and heterophily into anomaly-awan embeddings transferable across datasets. UNPrompt (Niu al.

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Aromaly
Rew Craedh
NBC
UNtromat
DNLLEVE EYE
Noma Mormal
Anomaly
Anoiatl Anomaly
Raw Graph
UNFrompt
Nomma Hormal
Normehnnemaly
Amuraay-huurall
Bew Graph
ABC
iNPromer
OWNLEYE
Figure 1: Performance Visualization SOTA GAD methods, denotes best. Top row: TSNE embeddings of Facebook and Weibo graph data for (a) original features, (b) ARC, (c) UNPrompt, and (d) OWLEYE (ours). ARC pushes the two graphs apart rather than aligning them. Middle and bottom rows: pairwise Euclidean distances for Normal-Normal Normal-Anomal Anomaly node pairs on Weibo and Facebook dataset, respectively. In the original graph (middle row, (a)), Normal-Normal pairs denser than Normal-Anomaly pairs Weibo dataset- important pattern reversed by UNPrompt (middle row, (c)). The existing data preprocessing methods fail to either align the graphs into the share space preserve important patterns after normalization.
and Anomaly-
2024
proposes generalized neighborhood prompts that leverage latent node attribute predictability as an anomaly score, enabling effective anomaly detection in previously unseen graphs.
However, several critical limitations still hinder the full potential of these generalist models. First, graphs from different domains often have inherently different feature spaces and semantic inter- pretations. Existing methods (Niu al. 2024 Liu et al. 2024 Qiao al.) (2025) typically use dimensionality reduction techniques such as principal component analysis (PCA) (Maćkiewicz & Ratajezak 1993) or singular value decomposition (SVD) (Hoecker & Kartvelishvili 1996), along with basic normalization strategies, to enforce shared input space with the same dimensionality. However, these heuristics frequently fail align heterogeneous feature distributions and preserve the important structural patterns effectively. In Figure D we visualize TSNE embeddings of Face- book and Weibo graphs before and after preprocessing with three methods (top row), along with pairwise Euclidean distances among three types of node pairs: Normal-Normal, Normal- Anoomaly and Goomaly-Anomasn
for both datasets (middle and bottom rows). Notably, the TSNE plots show that ARC (Liu et al. 2024) tends separate the graphs rather than align them, while UN- Prompt (Niu et al., 2024) disrupts critical structural patterns, for instance, reverses the density relationship between Normal-Norma, and Normal- Anomaly pairs observed in the original graph (compare subfigures in the middle row columns (a) and (c)). Second, current approaches lack mech- anisms for continual capabilities incremental update of normal and abnormal patterns without retraining from scratch. Third, many existing models assume the availability of few labeled nodes the target graph facilitate few- shot learning. In practice, however, labeling anomalies can be costly and requires domain expertise, making this assumption unrealistic. This raises an important question: how can we enable zero-shot anomaly detection without relying any labeled data from the test graph?
they do not support seamless integration of new graphs and the
To address these limitations, we propose OWLEYE, a novel generalist for zero-shot graph anomaly detection across multiple domains. In brief, the core idea of OWLEYE is to learn and store represen- tative patterns of normal behavior from multiple source graphs in a structured dictionary that acts knowledge base. When applied to unseen graph, OWLEYE by leveraging the representative patterns stored in the dictionary. Our approach is built on three key components. First, we introduce cross-domain feature alignment module, which normalizes
effectively detect anomalies
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and aligns node features across graphs using pairwise distance statistics, ensuring that graphs from different domains can be embedded into a shared imput feature space. Second, we develop a multi- domain pattern module that extracts both attribute-level and structure-level patterns from training graphs and stores them in a pattern dictionary. This dictionary enables the model to generalize unseen graphs together with the cross-domain feature alignment module. Third, truncated attention-based cross-domain reconstruction module that samples subset of nodes and reconstructs them using the stored patterns, effectively identifying anomalies while minimizing the influence of abnormal nodes during the reconstruction process.
design
2 OWLEYE: ZERO-SHOT CROSS-DOMAN GRAPH ANOMALY DETECTOR
In this section, we present OWLEYE and illustrate cross-domain feature alignment, multi-domain pattern learning, and truncated attention-based reconstruction. Throughout this paper, we use reg- ular letters to denote scalars (e.g., a), boldface lowercase letters to denote vectors (e.g., Φ), and boldface uppercase letters
denote matrices (e.g.. A). Let 9 EV, X) be an undirected graph, where V, E, X are the set of nodes, set of edges and the node attribute matrix, respectively. Let Ttrain = (Dirain: D2 train :..,...... Pirain3 } be set of training datasets with m graphs and each Di Dirain = (Iivain: Virain) ) is a labeled dataset, where Virain is label vector denoting the abnormal- ity of each node in the graph Girgin Our objective to train a GAD model on Ttrain to identify
anomalous nodes in the graph Tizst from the test datasets Trest (D)est: D Piest Diesth: }. where m' represents the number of graphs in the test datasets.
Amtiutte
lowe puttarna
Dictionary
Feeture Narmalizarion
Projection
Triricate
Tewel
Struccurt
Attemtion
Multi-domain Feature
Multi-domain Multi palti-pattern Representation Learning
Truncated Attention-
Alignment
Feature Reconstruction
Figure 2: Overview of OWLEYE.
2.1 PRESERVING DOMAIN-SPECIRES SEMANTICS IN PROSS-DOMANE FEATURE ALIGNMENT
Graph data from different domains often exhibit heterogeneous features that vary in both dimension- ality and semantic meaning. For example, nodes citation network may be described by textual content and paper metadata, whereas nodes in a social network might be characterized by user pro- file attributes. This heterogeneity poses a significant challenge for generalist GAD models, which require a consistent imput representation across domains. Therefore, how can we effectively unify and align heterogeneous features from diverse graph domains without compromising their semantic integrity? To address this issue, we first propose to project these features into common feature space to achieve the consistent feature dimension and then employ the cross-domain feature align- ment to align the features from different graphs into shared input space without compromising their semantic integrity.
Feature Projection. To achieve the consistent attribute dimension across different graphs, employ the principal component analysis (PCA) technique on the raw features of each graph Gi E Ttrain U Trest- Specifically, given the attribute matrix X E g= xa" from the graph G' with n' nodes, aim transform it to X e g=' xd with the common dimensionality of d by:
X Proj(X')
(1)
where Proj(.) is PCA.
Cross-domain Feature Normalization. Although PCA enables consistent dimensionality across graphs, the semantic meaning of each projected feature across different datasets remains distinct. As
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shown in Figure the TSNE plots show that ARC (Liu et al. 2024 tends to separate the two graphs rather than align them, while UNPrompt (Niu et al. 2024) distorts the input space by reversing the density relationship between Normal-Normal and graph (compare subfigures (a) and (c) the second row). To align attributes from different graphs into shared input space, propose cross-domain feature Normalization align the semantics and unify the distributions across graphs. Specifically, we first compute the average normalization of node features in the i-th graph G by N ==== # ΣjEN V E
pairs observed in the original
In the second row of Figure [ observe that large distance gap between Normal-Normal pair and a Normal-Anomaly pair on the Weibo dataset can be crucial pattern for a GAD model to find a proper decision boundary separate normal and anomalous nodes. This observation inspires to leverage the pairwise node distance for Normal-Norma pairs, Normal-Anomaly pairs and Anomaly- Anomaly pairs. However, the lack of label information for graph from the test set prevents us from fully benefiting from this observation. To relax the constraint of the unavailable label information, we propose measure the pairwise distance an important indicator for preserving the structural pattern in the latent space regardless of its label information. Specifically, we measure the pairwise node distance for the original graph and the normalized graph as follows:
dist
Σ B..PLEV.
(
at )2, disty
Σ V..
)2 N
(2)
(n²))
(n²)²
N
where dist' and dist'y measure the distance between the pairwise node distance for the original graph G and the normalized graph, respectively. To utilize the pairwise distance for the graphs from the training set, we take the median over all available graphs.
(3) The reason why we choose not to use the average operation is to avoid a situation where a graph with too large average pairwise distance dominates the datasets. Finally, we normalize the node attributes in the i-th graph as follows:
dist med median([dist) _dist ..., d)s dist median([distly, y dist"
X N max(ff.)
X ト
disc where f V dist distger
is a scaling factor to control the magnitude of the pairwise distance cross different graphs, and is the constant positive temperature, which is set to in the experiments.
2.2 MULTI-DOMAIN MULTI-PATTERN DICTIONARY LEARNING
One major limitation of existing generalist GAD models is their inability to support the seamless integration of knowledge extracted from new graphs, well as the incremental updating of nor- mal and abnormal patterns without retraining from scratch. To this end, can we design a generalist GAD framework that enables continual learning by efficiently accumulating and updating knowl- edge across diverse graph domains? Hence, we propose to learn and extract both attribute-level and structure-level patterns and store them dynamic dictionary.
Attribute-level Representation Learning. Following the existing generalist GAD methods (Niu
al. 2024 Liu et al. 2024 Qiao al. 2025), we aim learn and extract the generalized attribute-
level H + representations across graphs:
air
H H (A H Wi )
(S)
where A E Rnn xxx is the adjacency matrix of graph, Wi E RRdx is the learnable weight matrix attr of the 1-th layer graph neural network (Kipf & Welling 2017) to learn the attribute representations,
Hit R denotes the attribute-level embedding of the graph g and H X². To fully
capture the high-order neighborhood information, we concatenate the multi-hop information with the residual network (He et al. 2016):
H
H 12
H 1.1
H 11+1 sit
H 6.1
(6)
where H' xld
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Structure-level Representation Learning. The attribute-level representation H' captures node- specific information (c.g., a user's s interests in a social network), while it fails to capture relationships and interactions (e.g.. who connects to whom and how densely). For example, in social networks, two users may have similar attributes but vastly different roles based on their connectivity (e.g., normal users anomalous users). Thus, natural question arises: can learn the structural representations without the intervention of the node attributes? Inspired by this, we propose to first replace the raw node attributes with a d-dimension all-one vector and learn structural representations Ri as follows:
p2,+1 = (A''R ('
Ratnne W struc
where W E Rdxd is the learnable weight matrix of the I-th layer graph neural network to learn the structural representations, Ruine E B xd denotes the structural embedding of the graph G' and RiA 1 E Rn xd is the all-one matrix. Similarly, we concatenate the multi-hop information with the residual network to fully capture the high-order neighborhood information R':
R
R
R
R
R
Cross-domain Pattern Extraction. After learning both attribute-lvvel representation H and structure-level representation R for even available graph, we randomly extract Nsup patterns in total from each graph G and store them in two dictionaries by:
Dict, Hiix''), Diet' = [i'xx
where idx is set of the node index randomly sampled from the graph GJ. Due the fact that the patterns extracted from different graphs contribute differently for detecting anomalies in these graphs, we propose to measure the similarity of nodes between graph g and the patterns Dicr, R extracted from graph Go only based on the structure-level representation as follows:
sim Dict') max(softmaxx(P R ( [idx))
(10)
where sim( Dict E Rn measures the maximal node similarity between graph G and the pat- terns stored in Dict and W1 gidxddd is a weight matrix. Here, we expand sim Dict R=
to be gnt' xtd The reason why we only use structure-leve representation for similarity measurement that leveraging attribute-level representation may fail to distinguish the camouflaged anomalous nodes with the attributes similar to its normal neighbors. Notice that the advantages of extracting and storing the patterns in dictionary include that these representative patterns could be lever- aged for anomaly detection in the unseen graphs and that the dictionary could be easily updated by adding more patterns, thus enabling the continual evolving capabilities of OWLEYE. Similarly, with
sim( Diet'y) can be computed via by replacing the structure-Ievel representation attribute-level representation H. Three case studies in Section 3.4 verify that OWLEYE has excel- lent continual evolving capabilities and achieves better performance on test graphs on average by directly adding more patterns extracted from other new graphs to the dictionary without retraining or finetuning the model.
2.3 TRUNCATED INTENTION-BASEL FEATURE RECONSTRUCTION FOR IN-CONTEXT LEARNING AND INFERENCE
Many existing models (Niu al. 2024 Liu al., 2024 Qiao al. 2025) usually assume the avail- ability of few labeled nodes in the test graph to facilitate few-shot learning. However, labeling anomalies can be costly and requires domain expertise in practice, making this assumption unre- enable zero-shot anomaly detection without
alistic. When labels are hardly available, how relying on any labeled data from the test graph? naive solution is to randomly sample pscudo- support nodes from test graph as normal nodes due the fact that the vast majority of the nodes are normal.
However, it inevitably leads to an issue that abnormal nodes might be misleadingly labeled as the pseudo-suppor nodes, thus resulting in performance degradation. To address this issue, we propose truncated tteettoonbbsed reconstruction method to filter out the potential abnormal nodes and only
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select the most representative nodes to reconstruct both attribute-level representation and structure- level representation. Specifically, we propose to measure the truncated attention score for the query node from the graph g and the normal attribute-level patterns HJ from Dict J E (1....,m,) by:
(WW HT)T)
a("",
Vid
(11)
ag" Dicty lidxal = - 0o, where idxa = Top( ag )1
Sj soltmax((a((" //TA)))TT))
where WO E Ridxid, WK E Ridxid are two weight matrices shared across all i and j pairs, Top(; k) selects the top node indices to be truncated, and 7a is the temperature magnifying the sig-
nificance of the selected patterns. Notice that ag", ,Dicty) [idxa] -oo truncates the less represen- tative nodes for the attribute-level representation reconstruction after softmax operation. Similarly, ij for structure-level patterns R from Dict', following Equation 11 can compute the attention it.
Then, we reconstruct both the attribute-level and structure-level representation in the graph G with the normal patterns from m training graphs by:
m
H'
si Dictf)) @ (a Dict
m
(12)
m R'
D o (a Dict!)))
m
where ① denotes the Hadamard product. The intuition is that aim to use the normal patterns extracted from each training graph to reconstruct the node embedding in the graph g
Training. To optimize OWLEYE training sets Tirains aim to minimize the following objective function:
m
H H
y
H )T
Crecon
Σ ByEMi
H
H
Up EAi
Carillet
maxx [max([[Fi
H || - H 11
1 +, (13)
i=l MEEN
mamax (
Re 11- |
R 112 X2
Lirpllet + frecom
Where A/3 Luyly, 0} denotes the set of normal nodes, A' = (rylux = 1} is the set of anomalous nodes and the margin of the triplet loss. Lyecnn we minimize the instance-wing difference (maximize the similarity) between the attribute-level representation H., and the recon- structed attribute-level representation H for vj E W but maximize the difference (minimize the similarity) between the attribute-level representation Hi, and the reconstructed attribute-level rep- resentation Hi for by V and vs A. Compared with Lrewon minimizing Luiplet allows more vs. pairwise contrasting (e.g., Hr Hr )|2) for robust representation learning.
Inference. At the inference stage, we first extract Nsup normal patterns stored in the dictionar- ies Dicty and DictR each graph from the training set Trrain- Given a graph 9 from the test set Treats extract and store Nsup normal patterns from G in the dictionaries Dictæ and Dictæ for representation reconstruction by Equation (12). The anomaly score node vy is computed as follows:
So, = - H 2 BI -Re
(14)

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3 EXPERIMENTS
3.1 EXPERIMENTAL SETUP
Datasets. In the experiments, we train OWLEYE and the baseline methods on a group of graph datasets and test on another group of datasets. Following (Liu et al., 2024), the training graphs span across variety of domains, including social networks, citation networks, and e-commerce co-review networks, each of them with either injected anomalies or real anomalies. Therefore, we select one graph from each domain and randomly select one more dataset in the training set (e.g., CiteSeer). Specifically, the training datasets Traann consist of PubMed, CiteSeer, Questions, and YelpChi, while the testing datasets Trest consist of Cora, Flickr, ACM, BlogCatalog, Facebook, Weibo, Reddit, and Amazon. We also include the experimental results for different train-test split in Appendix B.2
Baselines. We compare our proposed method with supervised methods, unsupervised methods and one-for-all methods. Supervised methods include two state-of-the-arl GNNs specifically designed for GAD, i.e. BWGNN (Tang et al. 2022), and GHRN (Gao et al., (023). Unsupervised methods include four representative approaches with distinct designs, including the generative method DOM- 2019), the contrastive method SLGAD (Zheng et al. (021b). two affinity- -baee methods (c.g.. TAM (Qio & Pang 2023) and CARE (Zheng al. (025a)), one-for-all methods in-
INANT (Ding et al.
clude ARC (Liu et al.
2024) and UNPrompt (Niu et al. 2024).
Implementation Details. Following (Liu et al. 2024
Qiao & Pang 2023 Zheng al. 2025a
Liu et al. (022). we evaluate the performance of OWLEYE and baseline methods with respect to AUROC and AUPRC evaluation metrics for GAD. We report the average AUROC/AUPRC with standard deviations across 5 trials. We train ARC on all the datasets in Terain jointly, and evaluate the model on each dataset in Trest in zero shot setting. In the experiment, we set 1, Ta 0.001, Naup = 2000, X = 0.2, and B = 0.01.
Table 1:
Anomaly detection performance w.r.t AUPRC. We highlighted the results ranked first and second. "Average" indicates the average AUPRC over 8 datasets.
Method
Cora
Phekr
ACM
BlogCanlog
Facebook
Weibo
Redilt
Amazon
Averuge
Supervised (10-sbot)
BWGNN GHRN
9,57:1240
12.3912.68
13.3711603
12.97-3.15
5.8121117
9,552212
32112232
12.40:1.86
9.80
14.04-0733
16.45+2.59
16,29+1441
13.58-2.199
6,24+1.12
17.51 +1.52
4.44+1.15
13.84-263
12. 12,80=1.66 1,67
Unsupervised
(zor)
DOMINANT
31.77=034
28.7621152
32.49114.97
29.51=3.44
3.42:00.6
29.6310866
3.2810.37
36.80.8.37
24.46.:3.11
SLGAD
18:27-1001
16.93.16.20
1.33.10.3
9,471.300
0.93.0.23
35.801141
4.00112.77
5.3312.000
11.51:12.38
TAM
9.43+0027
23.34:1142
40.68:12.58
25 29.596
12.1813.14
23.01=15:14
4.221:022
45.26:24344
3..96
CARE
35.12-0023
25.6410.16
37.76.10.35
25.06_0010
5.52.00.4
40,701.0.4
3.17110.17
56.76.1144
28.72.10.44
ARC
45.20=1008
35.13::0:20
39.02:10.08
13
4,25::047
18:
4.20:10.25
20.48:689
30.74::1.25
UNPrompt
9.8412290
25.21
11.181167
18.24113.05
4.32.0..5
20.58.15.22
3.77.10.32
9.411.2.69
12.82.13.58
OWLEYE
43.94:0046
37.69:10.2
39.7510.13
34.99-031
5.62.11.17
60.9010021
4.2510.11
62.20::318
36.17:10.75
Table 2: Anomaly detection performance in 10-shot setting w.rt AUPRC. We highlighted the results ranked first and second. "Average" indicates the average AUPRC over 8 datasets.
Method
Cora
Flickr
ACM
BlogCatalog
Facchook
Weibo
Reddit
Amazon
Average
Supervised (10-sbot)
BWGNN GHRN
9.57+2240
12.39:268
13.37.66.03
12.97=3.15
5.81
9.55
3.21#2332
12.402136
9.80=2..1
14.04_0773
16.45
16.2911.411
13.58_2.11
6.2411.12
17.5111552
4.44.1.15
13.84-2663
12.80.1667
Unsupervised Finetune (10-shot)
DOMINANT
22.35-081
30.42+1.85
24.76+0088
34.82-0.88
4.1240.25
78.63=1.28
4.18=0064
8.86+0.09
26.02+0.85
SLGAD
19.38:1446
17.4615622
5.33:1139
11.67=2.22
3.81
36.23:::411
4.32:22113
7.69-282
13.24:2.116
14.27-065
27.68+1145
57.3245.26
27.49-100
11.73+2.34
26.78-n228
3.67=0.16
62.62:33.17
27,70 1.80
CARE
39.52-2900
27.19:0.24
38.12+0.45
27.75=006
5.86+20.69
44.3=0056
3.62=00155
59.51=1 1.2
30.74+90.1
48.02-0833
37.1510.24
39.1310.15
34.20-0027
4.92.0.75
4.32-10.16
21.90116322
UNPrompt OWLEYE
11.40=0:55
22.65+00300
14,80+0.70
18.01=12.88
4.04+11007
22.23-4433
3.85=021
11.08=0.44 46
13.5 13.51+22.62
44.40=1.58
38.32-0.15
39.1640.10
35.00=0034
6.83
64.14::225
4.96:0010
63.02-5577
1.37
3.2 EXPERIMENTAL RESULTS
Effectiveness Analysis. In the experiment, we evaluate our method in the zero-shot and 10-shot setting the graph from the test set Trest- For the supervised learning methods (c.g.. BWGNN and GHRN), we evaluate these two methods in the 10-shot setting, where we randomly sample normal nodes and 5 anomalies the support nodes. Table and Table show the performance of OWI.EYE
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and the baseline methods with respect to AUPRC in the zero-shot and 10-shot setting, respectively. The evaluation with respect to AUROC can be found in Tables 6 and Appendix [.1 Based on the results, we have the following observations: (1). OWLEYE demonstrates strong anomaly detection capability in the generalist GAD scenario, without any finetuning. The average AUPRC is 5% higher than the best competitor (i.e., ARC). (2). Even if two supervised methods (e.g., BWGNN and GHRN) are provided with 10-shot label information, our proposed method OWLEYE can still outperform these two methods on 6 out of 8 datasets with respect both AUPRC and AURPC. (3). If all methods are provided with 10-shot label information, OWLEYE achieves otate-ofthe-art performance on 4 out of in terms of AURPC and outperform the best competitor by more than 5%.
A
Ablation Study. We assess the ef- fectiveness of three key components in OWL.EY (e.g., cross-domain fea- ture normalization, structural pattern learning, and the truncated atten- tion module) by comparing it with three variants: OWLEYE-N (with- out feature normalization), OWL- EYB-S (without structural patterns), and OWI. (with standard atten- Figure 3: Left: Ablation Study. Right: Efficiency Analysis.
1.0
TAM
LAGAN
0.8
AND
5.000
35.33
0.6
34.05
0.4
0.2
0.0
tion instead of truneated attention). All methods are trained on the same set Tirain and evaluated on the test set Trest = {Cora, Flickr, ACM, BlogCatalog, Facebook, Weibo, Reddit, Amazon J. Figure 3 (Left) shows the average AUPRC across these eight datasets. OWLEYE consistently outperforms all variants, highlighting the importance of each component in achieving robust cross-domain graph anomaly detection.
Efficiency Analysis. To evaluate the runtime efficiency of OWLEYE, we compare the finetuming time of different methods on the ACM dataset. As shown Figure 3 (Right), OWLEYE achieves comparable finetuning time to other generalist GAD models, while significantly outperforming both unsupervised and supervised baselines (c.g.. TAM, CARE, BWGNN, GHRN) in terms of efficiency.
3.3 VISUALIZATION OF CROSS-ATTENTIR MAP ON CORA DATASETS
In this subsection, we provide a detailed visualization of the label matrices and cross-attention maps for the Cora dataset to better understand how our model distinguishes normal nodes from anomalous ones.
In Figure subfigures (a) and (b) display the cross-attention scores across the graphs, where the y-axis corresponds to the graph indices (0-3 indicating the four training graphs and 4 representing the test graph) and the x-axis denotes the ten extracted patterns learned for each graph. The top row shows the attribute attention and structural attention for normal node and the bottom row shows the attribute attention and structural attention for an anomalous one. Subfigure (a) shows the attention map when the model makes the correct prediction, while Subfigure (b) presents the attention map when the model makes the incorrect prediction. Subfigure (c) in both figures presents the ground- truth label matrices that specify whether each pattern is normal abnormal. Across both datasets, lighter colors such as light green and light yellow consistently indicate high similarity to the patterns associated with normal nodes, as reflected in the label matrices in Subfigure (c).
By examining these visualizations, we observe a clear and consistent relationship between the at- tention intensity and the correctness of the model's predictions: when the model correctly identifies a normal node, its attention map is dominated by light colors, suggesting a strong similarity to normal patterns stored in the dictionary: when correctly identifies an anomalous node, the atten- tion map becomes noticeably darker, indicating low similarity to normal behavior. Importantly, this trend reverses for misclassified nodes: normal nodes that are wrongly predicted as anomalies exhibit darker color in attention map, while misclassified anomalous nodes show lighter colors, showing the high similarity to those of normal nodes. This systematic behavior demonstrates that the attention map offers an intuitive and faithful interpretation mechanism, the color patterns directly reflect whether the node under consideration resembles the learned normal patterns, thereby revealing both the reasoning behind correct predictions and the failure modes behind incorrect ones.
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Label Matrix Visualization
Graph Attenttin Head
Node lineex
(a) Correctly Predicted Nodes
(b) Incorrectly Predicted Nodes
(c) Label Matrix
Figure 4: Visualization of cross-attention map on the Cora dataset. Subfigures (a) and (b) display the cross-attention scores across the graphs, where the y-axis corresponds the graph indices (0-3 indicating the four training graphs and 4 representing the test graph) and the x-axis denotes the ten extracted patterns learned for each graph. The top row shows the attribute attention and structural attention for normal node and the bottom row shows the attribute attention and structural attention for an anomalous one. Subfigure (c) in both figures presents the ground-truth label matrices that specify whether each node is normal or abnormal.
3.4 CASE STUDIES: ANALYSIS OWLEYE'S CONTINUAL LEARNING CAPABILITIES.
In this subsection, we present three case studies to evaluate how our proposed multi-domain pattern learning enhances OWLEYE's continual learning capability. Specifically, we con- (PubMed, Cora, Questions, YelpChi} and the testing set Trest = (Facebook, Weibo, Reddit, Amazon]. Additionally, a set of auxiliary graphs Touz = (Flickr, CiteSeer, BlogCatalog) is used for model enhancement through either pattern extraction or finetuning.
sider the training set Terain
Case Study 1: Pattern Augmen- tation without Finetuning. In this setting, we assess whether OWLEYE can improve performance by simply extracting patterns from new graphs without fine-tuning the model. We extract attribute- and structure-level patterns from graphs in Tauz and in- corporate them into the dictionaries.
Table 3: Case Study 1: Exploration of OWLEYE's contin- uous learning capability without finetuning. |Tawel indicates that patterns from graphs are added to the dic- tionaries.
Size of Tome
Facebook
Weibo
Reddit
Amazon
Average
Tous
6.72-1663
59.6320.96
3.93-0.08
54, 54,8824.55
31 11,29:1.75
Toux
6.32-1125
3.96+0.13
55.78+3.29
31,46+1.11
Tous
6.52=1.47
59,82=1.10
4.05
56.73-2.62
31.78
Yourl
7.04-1511
58.01-3442
32.27:1149
The results are shown in Table 3 where |Tanel = indicates that additional graphs are used
for pattern extraction and when [Taual 0, no new patterns are added. We observe a consistent performance improvement more patterns are incorporated, validating OWLEYE's abilitt to incre- mentally learn from new data sources in plug-and-pla fashion.
Case Study 2: Pattern Augmenta- tion with Finetuning. Next, we in- vestigate OWLEYE's continual learn- ing performance when finetuning is permitted using the graphs in Toun- Table presents the results un- der varying the number of graphs in Tauz. We find that OWLEYE achieves its highest average perfor-
Table 4: Case Study 2: Investigation of OWLEYE's con- tinual learning performance when finetuning is permitted using graphs in Tauz. |Tausl = O means that we do not finetune the model.
Size al Toue
Facebook
Weibo
Reddi
Amnzzon
Average
Toux
6.72=166
59.63:0.96
3.93=0.08
54.88=4.3 57
31 11.29:1.8
Toux
6.93-1899
59.35+0.55
3.93=0.13
55.74=361
31.48+1554
Your
6.69-1.42
56.5315.34
mance when finetuned on two graphs from Tanz- However, comparison with the results from Case Study reveals a notable insight: OWLEYE performs better without any fine-tuning simply by leveraging the added patterns. This highlights the effectiveness and practicality of our pattern- centric design for continual learning. We conjecture that the reason that finetuning the model fail to achieve better performance is that the model is hard train too many graphs and thus hard to converge. The visualization of the training loss vs epoch in two cases (e.g., |Tazz = and [Tanz 3) in Appendix C.3 shows that it's hard for the model converge when Tauz 3. 3.

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Published as a conference paper at ICLR 2026
Case Study 3: Impact of Dictionary Size. Finally, we analyze how the number of stored patterns (denoted as Neup)) affects performance. In the experiment, we use Ttrain = [PubMed, CiteSeer, Questions, YelpChi} for training, and let the test set be Trest {Cora, Flickr, ACM, BlogCatalog, Facebook, Weibo, Reddit, Amazon): ). Table reports the results. We observe that: (1) Increasing the number of patterns from to 200 leads to 0.55% performance gain; and (2) Beyond 200, the improvement becomes marginal and the performance becomes stable even if we keep increasing the dictionary size. These findings demonstrate that OWLEYE benefits from a larger pattern dictionary up to saturation point, beyond which rewards diminish.
Table 5: Case Study 3: AUPRC (%) under different dictionary sizes (i.e., Nsup).
Ryup
Cora
Flickr
ACM
BlogCatalog
Facebook
Weibo
Reddit
Amazoon
Average
10
44.55=0.48
37.86.0.26
39.5740.08
34.8420.33
5.63
61.3010.44
4.03=0.12
55.8912.16
35.4610.58
100
43.52=0.78
37.45-0.23
39.87+00006
34.76=0.38
5.43+11.34
61.13+0677
4.03=00.88
60.49+2.82
35.8440.779
200
43.24=0.50
37.65=0.29
39.92++0.12
34.72=00.3
5,32#1.40
60. 60.70=053
4.02=00.8
36.01+0.59 41.51
500
43.28=00.92
37 37.9620.39
39.86:20.13
34.79=0.35
5.43=1.32
61.00
4.04
62.77+1.36
36. 14:20.633
1000
43.8820.73
37.99
39.82:10:08
34.9220.399
1.33
60.52.0.79
4.03.0.12
61.7612.50
36.03:10.78
2000
43.94=0046
37.69:0.225
39.75
34.99=0.31
5.62+1117
60.90+0.21
4.25=0.11
62,20+3318
36.17:00733
RELATED WORK
Graph anomaly detection (GAD) is widely used in many applications (Grubbs 1969 Ma et al.
Pourhabibi et al. 2020, Li et al.) 202T; Duan et al. 2023) that naturally involve graph- structured data, such as transaction networks in financial fraud detection (Slipenchuk & Epishkina 2019 Ramakrishnan et al. 2019). communication and access networks in cybersecurity intrusion detection (Brdiczka et al., 2012; Duan et al. 2023), and user-user interaction graphs in fake news detection on social networks (Shu et a 2017 2019 Fu & He 2021). In recent years, the success of deep learning has spurred growing interest in developing deep learning-based GAD methods (Ma) ett al. 2023). Depending on the availability of labeled data, deep GAD approaches
2021
be broadly categorized into supervised and unsupervised methods (Qiao et al., 2024). In the unsupervised set- ting, graph contrastive learning methods (Zheng et al., 2021b Liu al. 2022 Chen et al. 2022 Jin et al. (02T] Xu et al. (2022) aim to learn effective node or graph-level representations by pulling similar instances together in the embedding space without any label information. Alternatively, reconstruction-basee
methods (Ding et al. [0019] Huang et al. 2023 Li 2011 et al. 2022 Peng et al. 2023) focus on learning dimensinnio embeddings capable of reconstructing input graph attributes or structures, with anomalies identified as instances exhibiting high reconstruc- tion errors. In the supervised setting, generative GNN- methods leverage label information to augment training data by synthesizing high-quality graph signals. Representative works include GraphSMOTE (Zhao et al. 202T), GraphMixup (Wu et al.] )022). and GraphENS (Park et al., 2022). which enhance model generalization and robustness. More recently, the advent of large language models (LLMs) has sparked a paradigm shift in AI research due to their strong generalization capa- bilities. Motivated by this, researchers exploring "one-for-all" generalist frameworks (Niu al., 2024 Liu et al. 2024) capable of adapting to diverse, unseen graph domains with minimal task- specific tuning. Cross-domain graph anomaly detection (CD-GAD) has recently drawn growing interest as models trained on one graph often degrade when deployed on graphs with different struc- tures or feature distributions (Ding et al., 202T Pirhayatifard & Silva). Wang al. (2023) proposed an anomaly-awar contrastive alignment approach that explicitly incorporates anomaly signals into cross-domain representation learning. improving robustness under heterogeneous domains. This paper also aims to develop such a generalist framework for GAD, while addressing several open challenges in existing approaches, including inadequate graph alignment across domains, lack of continual learning capabilities, and poor performance in zero-shot anomaly detection scenarios.
5 CONCLUSION
In this work, we presented OWLEYE, a novel generalist framework for zero-shot graph anomaly detection across multiple domains. To address the limitations of existing methods, such as poor feature alignment, lack of continual evolving capabilities, and reliance on labeled target data, OWL- EYE introduces structured and interpretable solution. By storing representative normal patterns in a reusable dictionary, OWLEYE enables scalable and effective anomaly detection on entirely un- seen graphs without retraining or target supervision. Extensive experiments real-world datasets validate the superior performance and transfcrability of OWLEYE over existing state-of- the-art art ap- proaches.
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ACKNOWLEDGMENT
This work is supported by National Science Foundation under Award No. IIS-2117902. The views and conclusions are those of the authors and should not be interpreted as representing the official policies of the funding agencies or the government.
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A IMPLEMENTATION DETAILS
The neural network structure of the proposed framework is 3-layer GCN. In the experiments, we set the initial learning rate be 3e-5, the hidden feature dimension be 512 and use Adam as the optimizer. The experiments are performed on Windows machine with a 24GB RTX 4090 GPU.
B ADDITIONAL EXPERIMENTAL RESULTS
B. ADDITIONAL EFFECTIVENESS ANALYSIS
In the experiment, we evaluate our method in the zero-shot and 10-shot setting on the graph from the test set Trest. For the supervised learning methods (c.g.. BWGNN and GHRN), we evaluate these two methods in the 10-shot setting, where randomly sample 5 normal nodes and 5 anomalies as the support nodes. Table 6 and ssho the performance of OWLEYE and the baseline methods with respect AUROC in the zero-shot and 10-shot setting, respectively. OWLEYE demonstrates strong anomaly detection capability in the generalist GAD scenario, without any finetuning. The average AUPRC better than the best competitor (i.e., GHRN).
Table 6: Anomaly detection performance in sero-shot setting w.r.t AUROC. We highlighted the results ranked first and second. "Average" indicates the average AUROC over 8 datasets.
Method
BlogCatalog Supervised (10-shot)
Reddi
Average
BWGNN GHRN
60.53-7776 76.59=3.55
69.40=9.22
70.37+13.29
68-74-6.00
67.74+11.28
61.28+2.58
63.68-8 69.76a5.52
69.75+5.81
66.44+7.49
71.96+4.19
76.83+44.35
72.73=4722
75.98+2.99
82,79+4.15
74.35a4.49
75.1244.25
Unsupervised
(acro-shot)
DOMINANT
85.75
73.43.1.12
73.6211.26
70.27:1233
52.3111.39
85,4211261
$3.4912.9
64.73.2.73
69.8813.199
SLGAD
72.73=0370
50.88422003
60.36:2.19 19
45.57
601.16+2..00
50.285:448
54.6341.25
TAM
57.85-0955
62.39.1.54
75.1612.68
62.3913355
63.3010.12
71.3311.077
$2.37.1022 0.21
76.04113.62
65. 65.10.11.55 1.58
CARE
66.92:002
67:90:10.70
70.95:10.75
45.41 59
79.18::1.69
86,3410.14
52.51:2005
83.09:10.65
69. 20.66
ARC
84.92-0058
72.34:10.16
77.54.0.13
73.65
65.03.10.79
88.43110.22
58.46.12.20
71.93.33.4
14.04.1.111
UNPrompt OWLEVE
63.55-3.09
69.67.1044
71.9811.38
67.8312.73
63 63.8717..7
47.4411..7
55.0111::20
56,7711673
62.01
79.52:0043
74.81+0097
78.20+0.12
74.83+0.06 76
64.85-00.77
87.66+0.14
58.03+066
85,43+4079
75.42:0.47
Table Anomaly detection performance in 10-shot setting w.r.t AUROC. We highlighted the results ranked first and second. "Average" indicates the average AUROC over 8 datasets.
Method
Cora
Flickr
ACM
BlogCatalog Supervised (10-shot)
Facebook
Weibo
Reddit
Amazon
Average
BWGNN GHRN
60.51-7.76 49.40-2222
70.37+13.29
67.74+11.28
61.28+2.58
21
69.75-5.81 74.35=a.49
66.44+7.49
76.59-3.55
71.96:4.19
76.83-44.35
72.73=472
75.98+2.99
82.799-..15
69.76=5.52
75.12+:4255
Unsupervised
(10-ahot)
DOMINANT
73.52-2047
73.84:12.76
73.82.10119
74.39-0.10 10
$1.22:10.85
91.6610.27
53.4811.87
20.39.2..80
69.04:11.54
SLGAD
73.39-0.44
65.15-1.33
53.55+1.82
62.67=11
51,40+2.51
61.64+1.91
53.17=3055
55.23-:1.36
59.40+1194
TAM
62.56-2.10
1.819
86.2911.57
63.6910.88
76,2613.70
71.7310.16
56.62.0.19
77.13:14.2
69.93.1192
CARE
67.27=2.44
68.81
71,53-20.11
53.95 99
79.94=10.28
87.65::3008
56 55:32=0048
83.80=33544
71.03+2291
85.28-0.38
74.62-00.28
77.84+0.17
73.58-0.33
67,28+1.18
87.04+1.36
60.06=1.21
73.79=3.16
UNPrompt
65.06:0044
69.16:10.47
73.24:10.45
28
67. 13:14.01
53.211:.16
55 05.69
62.14:10.9
64.32::1221
OWLEYE
79.71-0.72
75.77=1.36
78.06+0.34
74.42-0.33
66.94-+0.50
88.61+0.36
57.82-0.7
84.93-2.82
75.78+00.0
B.2 PERFORMANCE EVALUATION WITH DIFFERENT DATASET SPLIT
In the experiments, we evaluate OWLEYE and the bascline methods on different training and test set split. Specifically, the training datasets Terain consist of Reddit, BlogCatalog. Questions, and Cora, while the testing datasets Trest consist of Flickr, CiteSeer, PubMed, ACM, Facebook, Weibo, YelpChi, and Amazon. The experimental results are shown in Table g and Table 9 OWLEYE demonstrates strong anomaly detection capability in the generalist GAD scenario, without any fine- tuning. Specifically, OWLEYE achieves state-ofthe-art performance on 4 out of 8 datasets in terms of AUROC and out of 8 in terms of AURPC and demonstrates competitive performance the remainder.
B.3 CAN CROSS-DOMAIN FEATURE ALIGNMENT BENEFIT OTHER METHOD?
In this subsection, we want to answer the following question: Can cross-domain feature alignment benefit other method? We conducted experiments by replacing ARC's feature preprocessing with
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Table 8: Anomaly detection performance in zero-shot setting w.r. AUPRC. We highlighted the results ranked first and second. "Average" indicates the average AUPRC over datasets.
Method
Flickr
CheSeer
PuhMed
ACM
Weiho
Facebook
YelpChi
Amazon
Average
Supervised (10shhos)
BWGNN GHRN
12.39
10.31
11.631287
13.37.66033
9.55
5.811117
2.45.3.76
12.40
9.74.1.81
16.45=259
14.04=0.73
16.45+259
16.29:
17.51=1552
6.24
6.29+1441
13.84=2.3
13.39
Unsupervisel
(zero-shot)
DOMINANT
28.76:1155
18.7412.71
14.32.1.66
32.49114.7
29.6311.98
3.421086
4.73:1065
36.80.8.37
21.11
SLGAD
16.93-8.0
3.8-0083
11,07+2055
1,33+023
35.80=141
0.93+021
5.60+1.44
5.31+2200
10.1 +:2.08
TAM
23.34=142
8.80=088
23.71#1.22
40.68+2.58
23.01=15.14
4.18+142
6.19:0071
45.26=4.34
22.02+3.47
CARE
35.64-0.16
12.47.0.72
21.6241.29
37.7610.35
40,7040.74
5.524034
6.5120066
16.76=1.44
26.00
36.72-0144
45.94-0.39
28.61+00.15
63.05=0.75
5.39=045
5.26+0013
27.47-9.59
31,46+1.47
UNPrompt
24.35=0041
5.81=0.55
10.14:10.45
14.8920.67
31.43
3.31
7.99=2225
9.74
13.71
OWLEYE
37.69.0.15
43.1411.23
29.7240.43
59.3011.88
5.45.0.59
6,58.0.17
60.46.1647
35.27.1.39
Table 9: Anomaly detection performance in zero-shot setting w.r.t AUROC. We highlighted the
results ranked first and second. "Average" indicates the average AUROC over 8 datasets.
Method
Flickr
CiteSeer
PubMed
ACM
Weibo
Facebook
YelpChi
Amazon
Average
Supervised (10-shots)
BWGNN GHRN
69.40-9922
63.05
64.1615.49
70.37113.29
61 2,558
67.74111.28
54,2213.41
69.7515.81
65.00_6.84
71.96.4.19
77.5913.55
76.9114199
76.83114.35
82.79-4.15
15.98:12.99
53.83114.55
74.35114449
73.79-.378
Unsupervised
(2cro-shot)
DOMINANT
73.43=3.12
74.96+2.87
75.13+1.23
73.62+1.26
85.42=12.64
52.31+1.39
53.22++0.88
64.73+2273
69.12-3328
SLGAD
63.07=1.58
55.12::2.44
$6.51.+1002
50.88112003
60.16:0022
45.57:12.51
1..56111006
14.63
54,81=1.80
TAM
62.39-1.54
67.2911.38
77.8410.96
75.1612268
71.33_0007
63.30.10.09
52.4010.52
76.0413.62
68.22-11.35
CARE
67.90=0.70
69.43=00.7
66.55:10.97
70.9540.75
86.34=0.14
79.18:11.09
53,89:20.22
83.09:0042
72.42-0.50
ARC
74.12-0065
89.58.10.30
82.25100118
78.2810.32
87.15_0.66
66.3911161
54.13110.26
72.5416.78
75. 15.80.11.3
UNPrompt OWLEYE
69.15:0025
57.20::0.52
71.47:11449
72.68:10.33
66.94-5002
55 55.05-12.11
60.49:15.54
66.841:2.66
25.04-2.14
75.30_0098
84.47.00.58
77.98.0.25
78.5720.11
87.49
66.25:10.72
$4.73100.3
83.56.22.32
76.29.00.77
our proposed cross-domain feature alignment method. The results are shown in Table TO, The results demonstrate improved performance across out of 8 datasets, achieving an average increase of 0.57 in AUCROC and 1.01 in AUPRC. This validates the effectiveness of our proposed cross domain feature alignment.
Table 10: Effectiveness of Cross-domain Feature Alignment (FA)
Methou
Cora
Flickr
ACM
BlugCallog
Facebook
Weibo
Reddit
Amazon
Average
AUROX
ARC
84 = 0.58
72.34 + 0.16
77.54 + 0.13
74.65 = 0.55
65.03 + 0.79
88.43
0.62
58
2,20
71.93 + 3.84
74.04 + 1.11 74.61 0.94
ARC
84.06 = 0.31
73.37 + 0.21
77.84 0.23
74.92 =
64.93 0.55
88.81 0.75
59.17 0.72
74.74 4.51
Improvement
-0.86
0.30
-0.10
0.38
0.71
2,81
0.57
AUPRC 0.28
108
0.20
0.08
4.20
4
30.74
ARC
46.71 0.73
37.15
0.18
0.22
61.79 0.37
4.05 0.37
31.74
Imprrvvemenn
131
2022
0.82
131
0.37
-2.9
40.3
4.57
1.01
B.4 EXPERIMENTS ON IIGI ANOMALY RATE DATASET
In this subsection, we control Facebook data with synthetic anomaly rate. The raw anomaly rate of Facebook is 2.31% % and we manually inject 10% and 20% more anomalies. The results are shown Table IT The results (AUPRC) show that our method consistently outperforms two baseline methods when the anomalies rate increases.
B.5 EXPERIMENT RESULTS ABOUT SIGNIFICANT DOMAIN SHIFT
In this subsection, we evaluate our method under heavy domain shift setting. To simulate scenario where the e-commerce co-review domain is entirely unseen during training, we remove the YelpChi graph from the training set. This creates a substantial distribution mismatch, e-commerce co-review domain is available during model learning. the effect of removing YelpChi graph on Amazon graph as both of them from the same domain (e-commerce co-review domain), while the rest 10 graphs from other domains.
the test phase, we examine
the results reveal that, under this heavy domain shift, the performance on the Amazon graph decreases by 4.6% in AUPRC, while its AUROC remains largely unchanged. This behavior expected: with entire domain missing during training. domain-specifie patterns become harder to recover. Nevertheless, the performance drop remains moderate, demonstrating that the patterns extracted from other training graphs still help support robust. anomaly detection the Amazon
In Table 12
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Table 11: Performance of OWLEYE w.r. AUPRC with different anomaly rates
Anomaly Rate
2.31% + 0%
2.31% 10%
2.31% + 20%
UNPrompt
4.32=10.55
53.3810.56
70.09=10.28
ARC
4.25 =0.47
54.02 #0.41
71.92 #0.84
OWLEYE
4.86 +0.33
54.47 -0.82
72.47-10.48
dataset. Interestingly, for graphs from other domains, we observe slight improvements in AUROC and AUPRC when YelpChi is removed. For example, Cora's AUROC increases from 0.7952 to 0.7989 and AUPRC from 0.4394 to 0,4476, while Flickr's AUROC rises from 0.7481 to 0.7636. This trend consistent across several other nom-e-comerree graphs, suggesting that removing one domain might slightly reduce its influence during training, thereby allowing the model to better cap- ture patterns from other domains. Overall, these findings indicate that while heavy domain shift does lead to reasonable performance degradation for the affected domain, the cross cross-domant domain structural and attribute patterns captured by our model continue to provide meaningful generalization.
Table 12: Domain Shift Experiment Results %), testing on the Amazon graph. We remove the YelpChi graph from the e-commerce co-review domain to simulate significant domain shifts.
With YelpChi
Without YelpChi
Dataset
AUROC (%)
AUPRC (%)
AUROC (%)
AUPRC (%)
Cora
79.52 = 0.43
43.94 0.46
79.89 0.27
44.76 = 0.51
Flickr
74.81 # 0.97
37.69 0.25
76.36 2.55
37.91 # 0.38
ACM
78.20 = 0.12
39.75
78.93 # 0.87
39.72 0.15
BlogCatalog
74,83 0.26
$4.99
75.63 + 1.46
35.04 + 0.36
Facebook
0.33
5.62 1.17
66.27 = 0.97
6.19 1.37
Weibo
87.65 0.14
6.29
88.10
61.18
Reddit
58.02 0.68
4.25
57.79 0.22
3.97
Amazon
85.43 # 0.79
3.120
85.15 + 0.80
$7.64
B.6 EFFECTIVENESS STRUCTURAL REPRESENTATION
In Figure 3 (OwlEYy OWL.EYR-S), we have validated that including both attribute-level rep- resentation and structural-level representation indeed help successfully identify more anomalies. In this subsection, further verify the necessity of including structural representation in our model design. Table 13 shows the experimental results comparing using both structural similarity and attribute similarity (A+S) for domain similarity measurement vs only using structural similarity (S- Only) for domain similarity measurement. The experimental results show that using both structural similarity and attribute similarity (A+S) for domain similarity measurement decreases the perfor- mance. This suggests that using both structural and attribute similarity for domain similarity mea- surement is less stable than relying on structural similarity alone, because camouflaged anomalies may mimic normal neighbors' attributes and cross-domain feature discrepancies make reliable mea- surement more challenging. Table 14 shows that including structural patterns indeed increases the performance.
B.7 EFFECTIVENESS DIFFERENT FEATURE REDUCTION METHODS
In this subsection, we compared different linear and nonlinear feature projection methods including PCA, SVD, Kernel PCA, and NMF in Table T5 The experimental results show that our method with PCA still achieves the best performance as modeled in this paper. Comparing PCA with nonlinear methods like Kernel PCA and NMF, we observe that using more complicated feature projection does not necessarily improve the performance, as it might distort and misalign the original feature space, leading to performance drop. When the feature dimension is smaller than the preset projection dimension, we use Gaussian Random Projection to 256 following ARC and then do the feature reduction.
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Table 13: Comparison of using both attribute and structural similarity (A+S) vs structural similarity only (S-Only).
Dataset
A+S (%)
S-Only (%)
Improvement (%)
Cora
43.26 = 0.54
43.94 # 0.46
0.68
Flickr
37.83 + 0.39
37.69 + 0.25
-0.14
ACM
39.84 0.28
39.75 0.13
-0.09
BlogCatalog
34.34 0.53
$4.99
0.65
Facebook
6.35
5,62 # 1.17
-0.49
Weibo
38.61
60.90 + 0.21
2.28
Reddit
4.05 = 0.12
4.25 # 0.11
0.20
Amazon
48.01 # 18.44
62.20 # 3.18
14.19
Average
34.26 + 3.35
36.17 # 0.73
2.16
Table 14: Comparison of using No Structural Patterns vs OWLEYE for AUPRC.
Dataset
No Structural Patterns
OWLEYE
Improvement (%)
Cora
39.38 + 1.32
43.94 + 0.46
4.56
Flickr
38.06 = 0.08
37.69 + 0.25
-0.36
ACM
39.64 + 0.15
39.75 + 0.13
0.11
BlogCatalog
35.42 = 0.26
34.99 + 0.31
-0.43
Facebook
4.71 # 0.27
5.62 + 1.17
0.90
Weibo
57.18 # 1.09
60.90
3.72
Reddit
4.12 0.15
4,25 0.11
0.13
Amazon
61.98 # 1.08
22.20+3.15
0.22
Average
35.06 + 0.55
36.17 + 0.73
1.11
B.8 PATTERNS FROM ONE GRAPH VS MULTIPLE GRAPHS
this subsection, we aim to investigate whether incorporating patterns from multiple graphs can benefit anomaly detection. The results in Table [6 indicate that leveraging patterns from multiple graphs consistently improves performance compared to using patterns from a single target graph. Specifically, the average AUPRC across eight graphs increases by 0.32%, demonstrating that cross- graph knowledge can provide complementary information that helps identify anomalies more effec- tively. We observe that datasets such as Cora, Weibo, and Amazon benefit the most, with improve- ments of 0.73%, 0.43%, and 0,48%, respectively, suggesting that in domains with diverse structures or large graphs, shared patterns from multiple sources are particularly valuable. A few datasets, like ACM, show minor negative change (-0.09%), which may be attributed to the already sufficient patterns present the target graph, highlighting that the benefit of cross- cross-graph patterns depends on the intrinsic complexity and variability of the graph. Overall, these results empirically validate that maintaining structured dictionary with patterns from multiple graphs enhances the model's generalization capability for detecting anomalies in unseen domains.
B.9 MEDIAN OPERATION VS MEAN OPERATION IN FEATURE NORMALIZATION
In Table we compare the use of median and mean operations in our feature preprocessing module. (Table 19 shows the norm raw features in each graph.) The results show that replacing the median with the mean significantly reduces performance on Cora (43.63 + 34.95), CiteSeer (42.57 - 33.65), and Amazon (56.07 - 38.56), indicating that the mean is sensitive to graphs with unusually large feature norms, which can dominate the normalization process and distort the shared feature space. In contrast, median aggregation preserves the structural and semantic information across graphs, maintaining more stable performance all datasets (e.g., Flickr: 37.92 37.64, ACM: 39.55 vs 39.05). This demonstrates that using the median is more robust for heterogeneous graph domains, especially when feature distributions vary significantly across graphs.
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Table 15: Comparison of different feature preprocessing methods (AUPRC %).
Dataset
PCA
SVD
Kernel PCA
NMF
Cora
43.94 # 0.46
44.13 = 0.81
44.05 0.68
15.06 2.28
Flickr
37.69 # 0.25
38.18 0
3
33.09 0.70
ACM
39.75
39.18
38.75
32.28
BlogCatalog
34.99 0.31
3.31
33.36 0.29
Facebook
5.62
0.27
1.03
7.43 1.34
Weibo
60.90
2.48
0.25
49.90 3.66
Reddit
0.11
0.24
0.15
3,42 = 0.07
Amazon
(2.20
#4.27
38.75
20.61 #.57
Average 36.17 = 0.73
33.42 = 1.02
= 0.75
24.39 2.15
Table 16: Comparison of using patterns from the target graph only versus patterns from multiple graphs (AUPRC %).
Dataset
Patters from target graph only
Patterns from multiple graphs
Improvement
Cora
43.21 0.98
43.94 0.46
0.73
Flickr
37.41 = 0.16
37.69 # 0.25
0.28
ACM
39.84 0.23
39.75 0.13
-0,09
BlogCatalog
34.81 + 0.44
34.99 # 0.31
0.17
Facebook
5.27 # 1.34
5.62 + 1.17
0.35
Weibo
60.47 2 0.75
60.90 # 0.21
0,43
Reddit
4.03 + 0.08
4.25 + 0.11
0.23
Amazon
61.72 3.01
62.20 # 3.18
0.48
Average
35.84 0.88
36.17 0.73
0.32
B.10 ATYPICAL TRAINING DOMAIN EXPERIMENT
In Table we examine the robustness our method when training domain highly atypical by varying the training graphs. Since Weibo has the largest feature norm among all graphs shown in Table 19 we conduct two experiments with different training sets:
With Weibo: Pubmed, Questions, Weibo, and YelpChi;
With CiteSeer: Pubmed, Questions, CiteSeer, and YelpChi.
As shown in Table the results indicate that most datasets maintain consistent performance across the two settings. For instance, Cora achieves 43. 33.63% AUPRC with Weibo and 43.94% with CiteSeer, ACM scores 39.55% versus 39.75%, and BlogCatalog shows 35.00% versus 34.99% The average performance slightly higher when CiteSeer is included (32.63% vs. 31 .566). suggesting that our method is robust to the choice of highly atypical domain in the training set.
B.11 MORE EFFICIENCY ANALYSIS RESULTS
In Table 20 report the training time the ACM dataset for all baseline methods in the table. Following the experiment on efficiency analysis shown in Figure ACM selected in the exper- iment for the better comparison between training time and fine-tuning time. The results show that the training time of our method is more efficient than most of the baseline methods with overall first place performance gain reported in Tables and
B.12 PARAMETER SENSITIVITY ANALYSIS
In this section, conduct comprehensive sensitivity analysis of the key hyperparameters eight datasets, with the results summarized in Table 21 We focus on four hyperparameters: (1) the constant positive temperature (1,2, 3, 3),,, which controls the degree of feature normaliza-
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Table 17: AUPRC scores comparing median vs. mean aggregation.
Dataset
Median (AUPRC %)
Mean (AUPRC %)
Cora
43.63 1.94
34.95 11.96
Flickr
37.92 + 0.42
37.64 + 1.15
ACM
39.55 = 0.12
39.05 + 0.31
BlogCatalog
35.00 = 0.20
34.53
Facebook
4.56 0.71
4.30 1.15
CiteSeer
42.57 + 1.05
33.65 = 12.64
Reddit
4.19 0.14
3.98 # 0.16
Amazon
56.07 # 2.28
38.56 17.31 17.31
Average
32.94 = 0.86
28.33 + 5.66
Table 18: AUPRC scores when including patterns from Weibo vs. CiteSeer.
Dataset
With Weibo (AUPRC %)
With CiteSeer (AUPRC %)
Cora
43.63 # 1.94
43.94 + 0.46
Flickr
37.92 = 0.42
37.69 0.25
ACM
39.55 = 0.12
39.75 0.13
BlogCatalog
35.00 = 0.20
34.99 0.31
Facebook
4.56 0.71
5,62 1.17
Reddit
0.14
4.25 + 0.11
Amazon
56.07 2.28
62.20 3.18
Average
31.56 0.83
32.63 0.80
tion; (2) the truncated attention scaling factor To E {0.1, 0.01, 0.001. 0.0001), which magnifies the significance of the selected patterns; (3) the triplet loss margin E (0.1, 0.5, 1], which deter- mines the separation strength between positive and negative pairs; and (4) the balance coefficient E {1, 0.1, 0.01, 0.001 which controls the relative importance between graph structural features and graph attribute features.
The experimental results show that our method is highly robust to Ta. and X. Specifically, across all tested values of these parameters, the performance only varies slightly within the range 0.7502 to 0.7552. This indicates that the model remains stable under different choices of normalization temperature, attention scaling, and triplet loss margin. contrast, the parameter ,3 has a more pro- nounced impact. When 3 = the performance drops to 0. 0.7069, suggesting that overemphasizing one type of feature (structural feature) may degrade the model's ability to capture complementary information. As 3 decreases to 0.1, the performance improves significantly to 0.7556, and remains consistently around 7550 for smaller values. This demonstrates that a balanced contribution from both graph structural and attributed features is crucial for achieving optimal performance.
Overall, these results highlight two important findings: (i) our method exhibits strong robustness with respect to most hyperparameters, making it reliable in practical applications without heavy parameter tuning, and (ii) careful adjustment of the balance coefficient, / is particularly beneficial for enhancing performance by effectively leveraging the complementary strengths of structural and attributed graph information.
13 IMPACT DIFFERENT VALUES k
In this subsection, we evaluate the impact of different values of & in truncating the small values in truncated attention mechanism. In the experiment, & is relative value according to the dictionary size. We set the value of & to p% of patterns in the dictionary, where Nsup = 1000 for each graph. We vary the percentage of it from 5% to 50% as well
specific numbers of & (k 5 and = 10) and report the overall results across eight graphs below. Table (2 shows the impact of & in the truncated attention mechanism.
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Table 19: Feature norms of different graphs.
Dataset
Norm
Pubmed
0.24
Questions
0.45
Weibo
77.82
YelpChi
0.07
Cora
2.41
Flickr
0.25
ACM
0.10
BlogCatalog
0.30
Facebook
2.38
Citeseer
2.81
Reddit
0.02
Amazon
0.49
Table 20: Training time comparison (in seconds) across different methods.
Method
Training Time (s)
BWGNN
16.6
GHRN
8.72
SLGAD
280
Dominant
470
TAM
254.71
CARE
550.23
ARC
1.89
UNPrompt
21.23
OWLEYE(ours)
3.84
To be more specific, the range of the value of k is based the observation that the percentage of the anomaly is usually lower than 10%. Using the ratio of dictionary size can filter out most of the anomalies and then reduce the uncertainty. In situation where the percentage of anomalies is unusually high, we can manually increase the value of k. The results show that when we set the value of k to 10% 30% of patterns in the dictionary, the AUPRC scores are around 36. 1% and its performance decreases if we increase the percentage to 50%. One possible explanation is that we filter out too many patterns that might be useful for identifying anomalies. Similarly, when decreased the value of & to a value that could not filter out most of the anomalies, the performance starts to decrease, as method involves the anomalies for feature construction.
C VISUALIZATION
C.I ADDITIONAL FEATURE PREPROCESSING COMPARISON
In this subsection, we visualize the different feature preprocessing methods on Cora and Weibo datasets, showing how well different feature preprocessing methods preserve the graph structure for the graphs from two different domains. Notice that Weibo has the largest feature norm (77.82). while the norms of other graphs are less than 3. To better visualize the different data distribution in the raw graphs across different domains, Weibo selected. We also selected Cora, as it is citation network, different from Weibo social network. Similar to Figure [ we have the similar observation in Figure 5. where ARC pushes the two graphs apart rather than aligning them in the top row and UNPrompt reverses the different patterns of Normal-Normal pairs pair and Normal- Normal-Anomaly pairs in the middle row. In contrast, our method can fairly preserve the key graph structure even two graphs are from different domains.
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Table 21: Parameter Analysis on т, Tax X, and
Parameter
Cora
Flierr
ACM
BllgCaalog
Fueebook
Webbo
Redhit
Amiron
Average
76.77
80.51
1.71
75.12
1.38
78.20
0.78
75,45 1.44
64.03 1.65
87.46
0.49
57.73
1.76
K3.57 0.89
75.26
80.15 2: 1.09
75.94
3.37
78.54
0.63
75.18
1.09
66.26 2.78
87.40
0.84
57.86
a: 0.99
81.31 4: 2.89
75.21
1.71
80.50
1.35
76.02
3:46
78.67
0.80
75.19
1.12
61.99
$7.28
- 0.70
57.88
0.91
$1.48
5.89
75.23
0.1
79.06
1:0:0 0.26
75.19
1:40
77.95 1: 0.15
74.77
8.12
62
0.53
87.43
0.16
57.83
0.36
K5.33
0.84
75.05
0.48
0.01
79.32 1: 0.54
74.47
+ 0.29
78.17
4: 0.25
74.77
1: 0.13
63.46
0.46
87.53
-: 0.16
57.88
4: 0.61
$4.60
4: 0.83
75.02
0.41
0.001
76.97
0.73
73.65
0.25
77.91 1: 0.21
74.04
1 0.25
65.12.1..88 0.81
87.12
0.21
59.37
1 0.25
86.22
0.44
75.05
0.39
To
79.95
1.19
0-0.1
79.61
0..60
75.39
1.75
78.15 1107
74.84 + 0.21
0423--00.38
87.74
1120
58.41
K5.14 0.89
75.32
0.56
0.2
76.97
73.68
0.21
0.25
0.21
0.44
78.05
0.39
79.50
79.28
0.33
74.77
0.85
78.08 0,16
74.47
0.43
66.92 0.73
87.44
0.09
55,40 0.73
85.34 0.45
75.21 0.45
33
2.38
0.58
1.38
0.1
3 0.01
79.61
0.60
75.59
1.75
78.15
0.07
74.84
0.21
61.63 0.38
87.74
0.20
58.43
0:11
85.14
75.52 0.56
3 0.001
79.52
0.42
74.81
0.97
78.20 1: 0.12
74,83
1 0.26
64.85 : 0.337
87.67 :: 0.14
S8.02 1: 0.66
85.40 1: 0.81
75.41 0.47
Table 22: AUPRC scores under different values of k for truncated attention (in %).
Cora
Flickr
ACM
BlogCalalog
Facebook
Welbo
Raklit
Amnuon
Average
43.89 1.53
37.52 0.57
39.73 0.11
34.75
0.33
5.73
1.15
59.73
2.56
4.07 0.18
61.09 3.53
35,81
p.05p.aup
43.94
37.62
0..14
35.00 0.33
0.32
5.61
1.16
29.52
4.06 0.08
61.96
35.93 1.04
44.08 0.733
37.66 0.30
39.76 0..10
34.93 0.28
5.5
1.22
60.63
4.06 0.09
62.04
36,09 0.94
0.2
6..20
43.55
0.99
38.23
39.14+0.11
35.05
5,87 1.19
50.28 0.68
3.92 0.08
63,26 2.48
16.16
0.S
43.93
37.69 a 0.26
0..1
a.39
5.61 1.17
0.21
4.05 0.09
61.91 3.05
36.11 0.7.1
Normal
Anomaly
cora Anomally
Raw Graph
ARC
UNPromso
OVILEYE
Norma Normal
Normaldnomely
Raw Graph
UNPrompt
DVAL
Noma Normal
Normal Anomalyy
Anomalyy Anomaly
CANLEYE
Figure 5: Performance Visualization of SOTA GAD methods, denotes best. Top row: TSNE embeddings of Facebook and Weibo graph data for (a) original graph, (b) ARC, (c) UNPrompt, and (d) OWLEYE (ours). ARC pushes the two graphs apart rather than aligning them. Middle and bottom rows: pairwise Euclidean distances for Normal-Normal,, Normal-Anomaly, and Anomaly- Anomaly node pairs on Weibo and Cora dataset, respectively. In the original graph (middle row, (a)), Normal-Normal pairs denser than Normal-Anomaly pairs on Weibo dataset-an important pattern reversed by UNPrompt (middle row, (c)). The existing data preprocessing methods fail to either align the graphs into the share space or preserve important patterns after normalization.
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C.2 GRAPH SIMILARITY MEASUREMENT
We visualize the heat map for graph similarity measurement (defined in Equation 10 in Figure (Lefft). Specifically, we measure the graph similarity between a test graph (e.g., Amazon, Reddit, Weibo, BlogCatalog, Facebook, ACM, Flickr, Cora) and training graph (e.g., Pubmed, CiteSeer, Questions, YelpChi), where target-graph graph denotes the test graph where we extract and store pat- terns in Dictg- For instance, for Amazon graph, we measure the similarity between Amazon with four graphs in the training set Traain and the patterns sampled from Amazon graph, denoted as larget graph. We observe that all graphs from the test sets heavily rely on the patterns extracted from its own graph, while some graphs, such as Weiho, may also leverages the information from other graphs (e.g., questions) to detect the anomalies.
Atterdon
To Over Specht
033
KDK
12-
1.0
Figure 6: Left: Visualization of Graph Similarity Measurement for Different Graphs. Right: Train- ing Loss vs Epochs
C.3 TRAINING LOSS VS EPOCHS
We visualize the training loss the number of epochs in Figure (Right) as the auxiliary informa- tion for case study 2. In this figure, we observe that when no graph from Taur used to finetune the model, the training loss (i.c., blue line) drops dramatically. However, when we add all three graphs from Tour to finetune the model (i.e., the orange line), the training loss is much higher than that of model without using any graphs for model finetuning. This verify our conjecture that the reason that finetuning the model fail to achieve better performance is that the model is hard to train on too many graphs and thus hard converge.
C.4 ADDITIONAL ATTENTION MAP VISUALIZATION ON AMAZON DATASET
In this subsection, we provide a detailed visualization of the label matrices and cross-attention maps for the Amazon dataset to better understand how our model distinguishes normal nodes from anoma- lous ones. Similar to Figure 4 subfigures (a) and (b) display the cross-attention scores across the graphs, where the y-axis corresponds the graph indices (0-3 indicating the four training graphs and 4 representing the test graph) and the x-axis denotes the ten extracted patterns learned for each graph. The top row shows the attribute attention and structural attention for a normal node and the bottom row shows the attribute attention and structural attention for an anomalous one. Subfigure (a) shows the attention map when the model makes the correct prediction, while Subfigure (b) presents the attention map when the model makes the incorrect prediction. Subfigure (c) in both figures presents the ground-truth label matrices that specify whether each pattern is normal or abnormal. Across both datasets, lighter colors such light green and light yellow consistently indicate high similarity to the patterns associated with normal nodes, as reflected in the label matrices in Sub- figure (c). The attention maps show consistent pattern: correctly classified normal nodes have light-colored maps (high similarity to normal patterns), while correctly classified anomalies have darker maps (low similarity), and this pattern reverses for misclassified nodes.
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CorTeleo aherton
Label Matrix Visualization
Graph Attentton Head Index
Node Index
(a) Correctly Predicted Nodes
(b) Incorrectly Predicted Nodes
(c) Label Matrix
Figure 7: Visualization of cross-attention map on the Amazon dataset. Subfigures (a) and (b) display the cross-attention scores across the graphs, where the y-axis corresponds the graph indices (0-3 indicating the four training graphs and 4 representing the test graph) and the x-axis denotes the ten extracted patterns learned for each graph. The top row shows the attribute attention and structural attention for normal node and the bottom row shows the attribute attention and structural attention for an anomalous one. Subfigure (c) in both figures presents the ground-truth label matrices that specify whether each node is normal or abnormal.
D FUTURE DIRECTIONS
Learning on graphs has been hot research topic in the machine learning community, evolving from early pattern-based and statistical graph mining methods modern graph neural network mod-
els that enable end-to-end representation learning on complex relational data (Zhou et al., 2019
2020a 2022 Fu et al. 2022b 2023 Zheng al. e Fu et al. 2024 et al. 2025c Wang et al. 2025). Despite substantial progress, emerging real-world applications increasingly demand graph learning frameworks that can handle heterogeneous and multi-modal signals, weak or noisy
supervision, and rapidly evolving structures (Zheng et al.) 2019: Zhou et al. 2020b
Zheng al.
2021a
2022
2023
Ban et al. 2024 Tieu et al. 2024| Zheng et al. 2025b et al. 2025a Ning Fu et al. 2025; Tieu et al. 2025a b He al. 2025
In particular, multi-modal graph-base based anomaly detection has become a critical direction, where relational pat- must be jointly modeled with node- and edge-level attributes from diverse sources such text, time series, and images (Wu et al., 2024 Zhengg et al.) 2024a b) Lin et al. 2024 You et al. 2025). Looking ahead, important frontier lies developing graph foundation models for anomaly detec- tion, which aim to learn transferable, general-purposs representations from large-scale graphs and adapt them to downstream anomaly detection tasks under limited supervision (Zheng al. 2024d,
al.
2025 Li al. 2025b
Lan et al. 2024). Such foundation models have the potential to unify graph structure, multi-modi semantics, and anomaly-specific inductive biases, enabling robust detection of rare and evolving anomalous behaviors across domains.
E BROADER IMPACT
This paper does not have significant potential negative societal impacts and it proposes generalist. anomaly detection model, which aims detect anomalies in the unseen graphs. Our method can be applied to many applications, enhancing public safety by detecting fraud, cyberattacks, or suspicious activities.
26


Reviews:
[{"review_id": "U8d75OqLNg", "reviewer": "Reviewer_wzbY", "summary": "This paper proposes OWLEYE, a zero-shot cross-domain graph anomaly detection (GAD) model designed to establish a universal GAD framework. The core contribution lies in designing a novel cross-domain feature alignment module to address the limitations of existing generalist models when aligning graph data across different domains. Additionally, the model identifies anomalies through dictionary learning and a truncated attention-based reconstruction mechanism. Experimental results demonstrate superiority over baseline methods across multiple datasets.", "questions": "1. The paper employs PCA for feature projection but offers no justification for choosing this method over other nonlinear or domain adaptation techniques. Furthermore, how is the situation handled if the feature dimension of the original graph is smaller than the preset projection dimension `d`?\n\n2. During inference, the dictionary size and the `k` value in truncated attention are critical hyperparameters. Could the paper provide guiding principles or sensitivity analysis for selecting these parameters for new tasks? Were inference hyperparameters unified across all test sets in comparative experiments?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Zero-shot cross-domain graph anomaly detection is a significant and practical problem. The paper decomposes this challenge into three stages—feature alignment, pattern learning, and anomaly detection, addressing each sequentially to ensure a technically sound overall solution.\n2. The paper clearly identifies the shortcomings of existing general GAD models in feature alignment. It proposes a well-motivated, novel, and effective solution.\n3. Extensive experiments across multiple datasets demonstrate that OWLEYE significantly outperforms various state-of-the-art baselines. The case study on continuous learning also preliminarily shows the model's potential to absorb new knowledge in a plug-and-play manner.", "weaknesses": "1. The overall innovation of the framework is incremental. Its technical pipeline from feature alignment and multi-hop residual aggregation to a multi-pattern dictionary similar to “context learning” largely follows the established paradigm of generalist GAD models.\n2. Section 2.2 thoroughly argues for using “only structural similarity” (Equation 10) to address “camouflaged” anomalies. However, the final reconstruction formula (12) employs an undefined attribute-based similarity `sim(G, Dict_H)` during attribute reconstruction, contradicting prior descriptions (its own design rationale).\n3. The paper fails to explicitly describe the specific implementation of the “standard attention” variant in ablation study, nor does it provide a concrete analysis of its significant performance decline.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rJE4vjQJks", "reviewer": "Reviewer_fw57", "summary": "In this paper, the authors propose OWLEYE, an in-context graph anomaly detection model. OWLEYE’s design is based on three main components: (1) a PCA-based projection with normalization to align features across graphs; (2) a multi-domain dictionary learning approach that extracts attribute and structural patterns from graphs using GNNs; and (3) an anomaly score computation method based on reconstructing input graph patterns using the dictionary. The experiments compare OWLEYE against multiple supervised, unsupervised, and in-context (ARC and UNPrompt) graph anomaly detection methods using eight traditional benchmarks. The results show that OWLEYE achieves the best results on 3/8 datasets in the zero-shot setting and 4/8 datasets in the 10-shot setting. Additional results include an ablation study, an efficiency analysis, and case studies on pattern augmentation, fine-tuning, and dictionary size.", "questions": "1) Why is the feature alignment method proposed in Section 2.1 better than existing approaches?\n\n2) Is the loss from Equation 13 novel? What is the motivation?\n\n3) What are the values (numbers) in Figure 3?\n\n4) What information about the test graph is available in the problem setting considered?\n\n5) What is the replacement used for each component of OWLEYE in the ablation study? Are these components the state of the art?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper addresses a relevant and recent problem\n\n- The idea of using dictionary learning for in-context GAD is interesting\n\n- The proposed solution outperforms the baselines in most settings", "weaknesses": "- The paper writing could be greatly improved: Several parts of the paper concern me. Figure 1 is hard to read because of the small font and markers, and the overwhelming amount of information overall. The feature alignment method proposed in Section 2.1 is not well justified (why is it better than other alignment approaches?).  The loss from Equation 13 is also not well motivated (is it novel? Why is each element in the loss needed?). In general, Section 2 should include citations for all ideas that were inspired by previous work. Figure 3 can be misleading because some of the differences seem to be lower than 1%. The related work is very brief and doesn’t cover relevant literature, such as cross-domain graph anomaly detection, which can be unsupervised. \n\n- The problem setting is not completely clear: It is not clear what information about the test graph is available. In NLP, in-context learning usually assumes that only examples are given (not the entire dataset). However, this paper seems to assume that the entire graph is available during testing. The availability of the test graph makes a big difference, even without labels, as shown in the research on unsupervised GAD. My intuition is that in this setting, it makes more sense to start from an unsupervised method and then try to improve it by incorporating additional information from other labeled datasets (the graph topology is likely more informative than the 10 examples given as in-context input). Notice that if the test graph is not fully observed, the problem becomes much harder, and the approach I described does not work. \n\n- The experimental results need better insights: In the ablation study, it is not clear what the replacement is for each component of OWLEYE. For instance, ARC applies SVD and ordering instead of PCA and normalization for feature alignment. Anomaly score is computed using attention over in-context examples in ARC. The paper should make a case that each component of ARC is better than the alternative. It is also unclear why 200 patterns are sufficient to identify the anomalies; visualizing the patterns and anomalies could be helpful to understand what is happening (not clear if that is what is done in Fig. 1). There is no discussion on training time, which could be a deciding factor for practitioners.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qqIS7lWPfk", "reviewer": "Reviewer_aCfo", "summary": "This paper proposes a novel zero-shot graph anomaly detection framework called OWLEYE. The core idea of ​​OWLEYE is to learn and store representative patterns of normal behavior from multiple source graphs into a structured dictionary, thereby effectively detecting anomalies in unknown graph data without retraining.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper introduces a dynamic dictionary to store attribute-level and structure-level normal patterns, supporting incremental knowledge updates.\n\n2. Avoiding noise pollution from spurious normal node sampling is a crucial issue. Truncated attention mechanisms, as a soft filtering method, are more robust than random sampling or hard thresholding.\n\n3. Visualized feature analysis demonstrates a deep understanding of the data.", "weaknesses": "1. This paper claims that the runcated attention mechanism \"filters out potential abnormal nodes,\" but it doesn't provide theoretical analysis or comparisons with other attention mechanisms.\n\n2. Dictionary learning mechanisms implicitly assume \"pattern transferability,\" and I have concerns about the general applicability of this assumption.\n\n3. PCA is an unsupervised linear dimensionality reduction method. It can only guarantee dimensionality uniformity, but it cannot guarantee deep semantic alignment of features from different domains. This seems to imply the same assumption as the previous question: \"normal patterns in different domains are similar.\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i1IkojbUB0", "reviewer": "Reviewer_fjeF", "summary": "The paper proposes OWLEYE, a framework for zero-shot graph anomaly detection across multiple domains. OWLEYE consists of three key modules: (1) cross-domain feature alignment to harmonize representations from graphs with heterogeneous features, (2) a multi-domain dictionary learning scheme to store and transfer both attribute and structure-based graph patterns, and (3) a truncated attention-based reconstruction module for robust anomaly inference in unlabeled, unseen graphs. Experiments demonstrate consistent performance gains over recent baselines, including state-of-the-art generalist and few-shot GAD methods, across a suite of diverse real-world graph benchmarks. The paper further investigates continual pattern augmentation and efficiency, positioning OWLEYE as a scalable, label-efficient, and extensible approach for cross-domain GAD.", "questions": "see weakness", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "**S1** The paper directly tackles the problem of cross-domain, zero-shot graph anomaly detection (GAD), a critical and timely challenge as graph datasets become more diverse and distributed in real applications.\n\n**S2** The proposed pipeline—starting with feature projection/normalization (Section 2.1), followed by hierarchical pattern learning (Section 2.2), and concluding with a detailed in-context truncated attention mechanism (Section 2.3)—is thoughtfully constructed and mathematically grounded. The technical exposition of each module is presented with clarity and includes relevant equations (e.g., Equation for cross-domain normalization, attention, loss terms), supporting reproducibility.\n\n**S3** The inclusion of comprehensive experimental results (see Table 1, Table 2, Table 6, Table 7), as well as meaningful visualizations (notably Figure 1), clearly demonstrate the effectiveness of OWLEYE in aligning cross-domain graphs and preserving crucial anomaly-detection patterns, something recent models like ARC and UNPrompt struggle with.", "weaknesses": "**W1**While the method is described mathematically, there is little depth to the theoretical guarantees for convergence or for the effectiveness of the normalization and attention truncation mechanisms. For example, the normalization equations on Page 4 define a cross-domain scaling scheme, but the paper does not provide a formal argument or sufficient empirical diagnostic to show that this process consistently preserves key structural semantics across arbitrary domains, beyond the handful of visual examples (Figure 1). Similarly, the attention-truncation approach is intuitively appealing but lacks any statistical justification concerning which anomalies could be missed or mistakenly preserved when top-k truncation is used, particularly as graph scale increases or distributions shift.\n\n**W2**The core pattern storage and reconstruction process relies on “random sampling” nodes and computing truncated attention across dictionaries, but the sensitivity and robustness to this random selection are not systematically analyzed. Figure 4 visualizes attention distributions, but there is no supporting analysis as to how much predictive performance can vary across different random seeds or pattern samples, especially given the use of random support set extraction for the dictionary.\n\n**W3**The method is described as storing “interpretable patterns” in dictionaries, but no concrete analysis or visualization is provided showing what these patterns are, how they differ across domains, or whether they correspond to genuine “normal” behaviors as claimed. Figure 2 shows the architecture, and Figure 4 provides a heat map, but neither supplies users with diagnostics to interpret failures or understand the significance of stored patterns, this is especially important in high-stakes GAD applications.\n\n**W4**Although a broad set of baselines are used, the paper does not explain why several recently proposed cross-domain learning GNNs (many from the missing related work below) were not attempted as baselines, or even as ablation trims to OWLEYE.\nHyperparameter Sensitivity: While Table 10 (Appendix) selects some hyperparameter sweeps, there is little qualitative discussion of failure modes or special behavior, e.g., what happens if the size of the pattern dictionary is much smaller, or how performance degrades with heavy domain shift or label imbalance between training and test graphs.\n\n**W5**Some mathematical definitions, e.g., in the attribute-level and structure-level pattern extraction (Section 2.2 equations), could use more explicit notation (e.g., variable range explanations) for clarity, especially for readers less familiar with GCN-based operations.\n\n**W6**The cross-domain normalization claims median-based scaling (Equation Page 4) avoids domination by single datasets, but this is not shown in controlled experiments (e.g., single domain greatly divergent in distribution), how robust is this alignment when a domain is highly atypical? No “stress tests” or outlier cases are given.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)