Pith. sign in

REVIEW 2 major objections 1 minor 56 references

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection

T0 review · 2 major / 1 minor · reviewed 2026-06-29 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read SignGAD reformulates graph anomaly detection as agent-designed task-specific workflows instead of fixed detectors.

desk verdict SignGAD reframes few-shot graph anomaly detection as agent-designed workflows plus a guarded refit, but the abstract supplies no evidence that the refit avoids fitting the same small label set used for selection. read the letter →

arxiv 2605.27470 v1 pith:OU6HB4VL submitted 2026-05-26 cs.LG cs.AI

Tairan Huang , Qiang Chen , Yili Wang , Yueyue Ma , Changlong He , Xiu Su , Yi Chen This is my paper · ORCID
classification cs.LGcs.AI
keywords graphanomalydetectionfew-shotlearningagenticworkflowsself-designingsystemsneuralnetworksworkflowdesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper establishes that fixed pipelines in graph anomaly detection restrict adaptability across tasks when supervision is limited and fail to incorporate contextual and structural signals explicitly. It demonstrates that an agent can instead construct detection workflows to select graph encodings and detector designs suited to each task's anomaly evidence. A guarded final refit strategy then calibrates the workflow by adjusting refit acceptance to improve reliability. This shift matters because it allows the system to handle varied graph structures and anomaly patterns more effectively with few labels. Experiments on real-world datasets show the approach yields stronger results than prior methods.

What carries the argument

task-conditioned detection workflows that select graph encodings and detector designs to capture contextual and structural anomaly signals

What would settle it

Compare detection performance on the same few-shot datasets when the agent's workflow selections are replaced by fixed or random choices of encodings and detectors; a large drop would indicate the selections do not drive the gains.

Watch

Extended reading notes

Core claim

SignGAD reformulates the graph anomaly detection task from training a fixed anomaly detector to designing task-conditioned detection workflows. By constructing these workflows, the framework selects suitable graph encodings and detector designs to exploit task-specific anomaly evidence. It further introduces a guarded final refit strategy that refines the selected workflow by calibrating refit acceptance, which enhances reliability under limited supervision.

Load-bearing premise

An agent can reliably design and select workflows that capture the relevant anomaly signals from few available labels without the selection process introducing bias or overfitting.

Editorial extensions

If this is right

  • Detection systems adapt to different graph tasks under limited supervision by choosing appropriate components for each case.
  • Explicit use of contextual and structural anomaly signals becomes possible through the workflow choices.
  • The guarded refit strategy improves reliability by controlling acceptance of workflow adjustments.
  • Performance exceeds that of state-of-the-art fixed-pipeline methods on multiple real-world datasets.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same workflow-construction approach could apply to other few-shot graph tasks such as node classification or link prediction.
  • Agent-designed workflows may reduce the manual effort needed to tune graph neural network components for anomaly settings.
  • The method could be tested on graphs that change over time to check whether new workflows are generated as anomaly patterns evolve.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes SignGAD, a framework that reformulates few-shot graph anomaly detection as the agentic design of task-conditioned workflows. These workflows select graph encodings and detector designs to exploit contextual and structural anomaly signals; a guarded final refit strategy is introduced to calibrate acceptance under limited supervision. The authors report that the resulting method outperforms state-of-the-art baselines on several real-world datasets.

Significance. If the central claim holds without selection bias in the workflow/refit loop, the work would be significant for replacing fixed GAD pipelines with an adaptive, evidence-exploiting workflow paradigm that is explicitly designed for the few-shot regime. No machine-checked proofs, open reproducible code, or parameter-free derivations are mentioned.

major comments (2)
  1. [Abstract] Abstract (and §3–4, method description): the guarded final refit strategy is described only as 'calibrating refit acceptance,' with no indication that acceptance decisions are made on labels held completely out of the workflow-selection loop. Because the same few-shot labels are the only supervision available, this omission directly threatens the claim that performance gains arise from genuine exploitation of task-specific signals rather than selection bias.
  2. [Experiments] Experiments section (tables/figures reporting results): no quantitative details are supplied on the number of labeled nodes used for workflow selection versus refit calibration, the exact baselines, or the metrics. Without these, the data-to-claim link for 'strong performance against state-of-the-art methods' cannot be evaluated and is load-bearing for the central contribution.
minor comments (1)
  1. Notation for the agentic workflow components is introduced without a compact summary table or diagram that would allow readers to trace the flow from task description to final detector output.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript accordingly to improve clarity and experimental reporting.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and §3–4, method description): the guarded final refit strategy is described only as 'calibrating refit acceptance,' with no indication that acceptance decisions are made on labels held completely out of the workflow-selection loop. Because the same few-shot labels are the only supervision available, this omission directly threatens the claim that performance gains arise from genuine exploitation of task-specific signals rather than selection bias.

    Authors: We agree that the description is insufficiently explicit on label usage. In the revised manuscript we will expand the abstract and §§3–4 to state that the few-shot labels are partitioned into disjoint subsets: one subset is used exclusively for workflow design and selection, while a completely held-out subset is reserved for the guarded refit calibration and acceptance decisions. This partitioning will be illustrated with pseudocode and a diagram to demonstrate that acceptance is independent of the selection loop. revision: yes

  2. Referee: [Experiments] Experiments section (tables/figures reporting results): no quantitative details are supplied on the number of labeled nodes used for workflow selection versus refit calibration, the exact baselines, or the metrics. Without these, the data-to-claim link for 'strong performance against state-of-the-art methods' cannot be evaluated and is load-bearing for the central contribution.

    Authors: We acknowledge that the current experimental reporting lacks the requested quantitative specifics. The revised experiments section will report, for each dataset and few-shot setting: (i) the exact counts of labeled nodes allocated to workflow selection versus refit calibration, (ii) the full list of baseline methods together with their hyper-parameter settings, and (iii) the precise evaluation metrics (AUC, F1, etc.). Tables and figures will be updated to present these quantities explicitly. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation remains self-contained

full rationale

The abstract and description outline an agentic workflow design process plus a guarded refit strategy for few-shot graph anomaly detection, with performance evaluated on real-world datasets. No equations, self-definitional reductions, fitted parameters renamed as independent predictions, or load-bearing self-citation chains are present in the provided text. The central claims rest on empirical selection of encodings/detectors and refit calibration rather than any tautological mapping back to inputs by construction. This is the expected honest non-finding when no specific reduction can be exhibited.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities beyond the high-level claim of agentic workflow design and guarded refit; all such elements remain unidentified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection." pith.science (2026). https://pith.science/paper/OU6HB4VL

@misc{pith2026260527470,
  author       = {Pith},
  title        = {Pith review of: Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OU6HB4VL}},
  note         = {Machine review of arXiv:2605.27470}
}
read the original abstract

Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection methods still face two key challenges: 1) fixed pipelines, which restrict their adaptability across different graph tasks under limited supervision; 2) weak evidence, which prevents them from explicitly incorporating contextual and structural anomaly signals into the detection process. In this paper, we propose a novel framework, self-designing agentic workflows for few-shot graph anomaly detection (SignGAD). Specifically, we propose a novel paradigm that reformulates graph anomaly detection task from training a fixed anomaly detector to designing task-conditioned detection workflows. By constructing detection workflows, SignGAD selects suitable graph encodings and detector designs to exploit task-specific anomaly evidence. Meanwhile, we introduce a guarded final refit strategy to refine the selected workflow by calibrating refit acceptance, enhancing reliability under limited supervision. Extensive experiments conducted on several real-world datasets demonstrate that SignGAD achieves strong performance against state-of-the-art methods, highlighting its effectiveness on graph anomaly detection tasks.

Figures

Figures reproduced from arXiv: 2605.27470 by the authors.

Figure 1
Figure 1. (a-b) Conceptual illustrations of the fundamental limitations in current GAD paradigms: fixed pipelines and weak evidence. (c) briefly describes our proposed method. As evidenced in (d), our method maintains impressive efficiency and delivers robust performance. 1. Introduction Graph anomaly detection (GAD) has been widely applied to many real-world detection scenarios, such as social networks [27, 49], e-commerce p… view at source ↗
Figure 2
Figure 2. The overall architecture of SignGAD, including (a) Task-Conditioned Workflow Construction, (b) Evidence Graph Encoding, (c) Workflow Detector Bank, (d) Validation Workflow Search, and (e) Guarded Final Refit. settings [21, 37]. More recently, large language models have been introduced to enhance GAD with semantic understanding and task-level reasoning [18]. Despite these advances, most methods still follow predefine… view at source ↗
Figure 3
Figure 3. The visualization analysis on the Amazon dataset, where red represents fraudsters blue represents benign entities. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The time cost comparison on four datasets. supervision, but also practical for large-scale graph anomaly detection. Visualization Analysis. To illustrate the effectiveness of the proposed method in a more intuitive way, we conduct the visualization task on the Amazon d…
Figure 5
Figure 5. Figure 5: The detailed textual description of Amazon dataset. Textual Description of YelpChi Dataset domain: review fraud detection dataset name: YelpChi / YelpChi-Fraud node semantics: reviews posted on Yelp for local businesses such as hotels and restaurants edge semantics: be…
Figure 6
Figure 6. Figure 6: The detailed textual description of YelpChi dataset. • YelpChi [29] aims to identify fraud comments for hotels and restaurants in Chicago. The detailed textual description is shown in [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: The detailed textual description of T-Finance dataset. Textual Description of T-Social Dataset domain: social network anomaly detection dataset name: T-Social / TSocial node semantics: unique anonymized accounts in a social network edge semantics: long-term friendship …
Figure 8
Figure 8. Figure 8: The detailed textual description of T-Social dataset. • GraphConsis [25] is a heterogeneous graph neural network designed to address three types of inconsistencies in the graph anomaly detection task: context, feature, and relation inconsistencies. • PC-GNN [23] is a s…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

56 extracted references · 2 canonical work pages

  1. [1]

    Optimizing agentic workflows using meta-tools.arXiv preprint arXiv:2601.22037, 2026

    Sami Abuzakuk, Anne-Marie Kermarrec, Rishi Sharma, Rasmus Moorits Veski, and Martijn de Vos. Optimizing agentic workflows using meta-tools.arXiv preprint arXiv:2601.22037, 2026

    work page arXiv 2026

  2. [2]

    Semi-supervised graph anomaly detection via robust homophily learning.Advances in Neural Information Processing Systems, 38:133988–134013, 2026

    Guoguo Ai, Hezhe Qiao, Hui Yan, and Guansong Pang. Semi-supervised graph anomaly detection via robust homophily learning.Advances in Neural Information Processing Systems, 38:133988–134013, 2026

    2026

  3. [3]

    Geoflow: Agentic workflow automation for geospatial tasks

    Amulya Bhattaram, Justin Chung, Stanley Chung, Ranit Gupta, Janani Ramamoorthy, Kartikeya Gullapalli, Diana Marculescu, and Dimitrios Stamoulis. Geoflow: Agentic workflow automation for geospatial tasks. InProceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, pages 1150–1153, 2025

    2025

  4. [4]

    How attentive are graph attention networks? InInternational Conference on Learning Representations, pages 1315–1324, 2022

    Shaked Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? InInternational Conference on Learning Representations, pages 1315–1324, 2022

    2022

  5. [5]

    Towards multiple missing values- resistant unsupervised graph anomaly detection

    Jiazhen Chen, Xiuqin Liang, Sichao Fu, Zheng Ma, and Weihua Ou. Towards multiple missing values- resistant unsupervised graph anomaly detection. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 20100–20108, 2026

    2026

  6. [6]

    Consistency training with learnable data augmentation for graph anomaly detection with limited supervision

    Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, and Jia Chen. Consistency training with learnable data augmentation for graph anomaly detection with limited supervision. In International Conference on Learning Representations, pages 137–142, 2024

    2024

  7. [7]

    Agent4edu: Advancing ai for education with agentic workflows

    Ling Dai, Yuan-Hao Jiang, Yuanyuan Chen, Zinuo Guo, Tian-Yi Liu, and Xiaobao Shao. Agent4edu: Advancing ai for education with agentic workflows. InProceedings of the 2024 3rd International Conference on Artificial Intelligence and Education, pages 180–185, 2024

    2024

  8. [8]

    Spacegnn: Multi-space graph neural network for node anomaly detection with extremely limited labels

    Xiangyu Dong, Xingyi Zhang, Lei Chen, Mingxuan Yuan, and Sibo Wang. Spacegnn: Multi-space graph neural network for node anomaly detection with extremely limited labels. InInternational Conference on Learning Representations, pages 117–122, 2025

    2025

Show all 56 references
  1. [9]

    Enhancing graph neural network-based fraud detectors against camouflaged fraudsters

    Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S Yu. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. InProceedings of the ACM International Conference on Information & Knowledge Management, pages 315–324, 2020

    2020

  2. [10]

    Alleviating structural distribution shift in graph anomaly detection

    Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang. Alleviating structural distribution shift in graph anomaly detection. InProceedings of the ACM International Conference on Web Search and Data Mining, pages 357–365, 2023

    2023

  3. [11]

    Addressing heterophily in graph anomaly detection: A perspective of graph spectrum

    YuanGao, XiangWang, XiangnanHe, ZhenguangLiu, HuaminFeng, andYongdongZhang. Addressing heterophily in graph anomaly detection: A perspective of graph spectrum. InProceedings of the World Wide Web, pages 1528–1538, 2023

    2023

  4. [12]

    Naast-gnn: Neighborhood adaptive aggregation and spectral tuning for graph anomaly detection

    Ronghui Guo, Xiaowang Zhang, Zhizhi Yu, Minghui Zou, Sai Zhang, and Zhiyong Feng. Naast-gnn: Neighborhood adaptive aggregation and spectral tuning for graph anomaly detection. InProceedings of the International Joint Conference on Artificial Intelligence, pages 2847–2855, 2025

    2025

  5. [13]

    Topological anomaly quantification for semi-supervised graph anomaly detection

    Ting Guo, Yangrui Fan, Caixia Cui, Jiye Liang, Jiao Zhao, and Da Wang. Topological anomaly quantification for semi-supervised graph anomaly detection. InInternational Conference on Learning Representations, 2026. 12 Detect by Yourself: Self-Designing Agentic Workflows for Few-...

    2026

  6. [14]

    Inductive representation learning on large graphs

    Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30:1071–1195, 2017

    2017

  7. [15]

    Tdflow: Agentic workflows for test driven development

    Kevin Han, Siddharth Maddikayala, Tim Knappe, Om Patel, Austen Liao, and Amir Barati Farimani. Tdflow: Agentic workflows for test driven development. InProceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long P...

    2026

  8. [16]

    Tempasd: Temporal anomalous subgraph discovery in large-scale dynamic financial networks

    Xiaolin Han, Yikun Zhang, Chenhao Ma, Lingyun Song, Reynold Cheng, and Xuequn Shang. Tempasd: Temporal anomalous subgraph discovery in large-scale dynamic financial networks. InProceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 826–837, 2025

    2025

  9. [17]

    Revisiting low- homophily for graph-based fraud detection.Neural Networks, 188:107407, 2025

    Tairan Huang, Qiutong Li, Cong Xu, Jianliang Gao, Zhao Li, and Shichao Zhang. Revisiting low- homophily for graph-based fraud detection.Neural Networks, 188:107407, 2025

    2025

  10. [18]

    Can llms find fraudsters? multi-level llm enhanced graph fraud detection

    Tairan Huang, Yili Wang, Qiutong Li, Changlong He, and Jianliang Gao. Can llms find fraudsters? multi-level llm enhanced graph fraud detection. InProceedings of the ACM International Conference on Multimedia, pages 1530–1538, 2025

    2025

  11. [19]

    Rethinking reconstruction-based graph-level anomaly detection: limitations and a simple remedy

    Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, and Kijung Shin. Rethinking reconstruction-based graph-level anomaly detection: limitations and a simple remedy. Advances in Neural Information Processing Systems, 37:95931–95962, 2024

    2024

  12. [20]

    Semi-supervised classification with graph convolutional networks

    Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. InInternational Conference on Learning Representations, pages 1031–1049, 2017

    2017

  13. [21]

    Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning

    Jongha Lee, Sunwoo Kim, and Kijung Shin. Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning. InProceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1506–1517, 2024

    2024

  14. [22]

    Dual-augment graph neural network for fraud detection

    Qiutong Li, Yanshen He, Cong Xu, Feng Wu, Jianliang Gao, and Zhao Li. Dual-augment graph neural network for fraud detection. InProceedings of the ACM International Conference on Information and Knowledge Management, pages 4188–4192, 2022

    2022

  15. [23]

    Pick and choose: a gnn-based imbalanced learning approach for fraud detection

    Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, and Qing He. Pick and choose: a gnn-based imbalanced learning approach for fraud detection. InProceedings of the World Wide Web, pages 3168–3177, 2021

    2021

  16. [24]

    Multi-dimensional adaptive mix-hop contextual learning framework for universal graph anomaly detection

    Zhaowei Liu, Leilei Jiang, and Haitao Yang. Multi-dimensional adaptive mix-hop contextual learning framework for universal graph anomaly detection. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 15412–15420, 2026

    2026

  17. [25]

    Alleviating the inconsistency problem of applying graph neural network to fraud detection

    Zhiwei Liu, Yingtong Dou, Philip S Yu, Yutong Deng, and Hao Peng. Alleviating the inconsistency problem of applying graph neural network to fraud detection. InProceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1569...

    2020

  18. [26]

    From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews

    Julian John McAuley and Jure Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. InProceedings of the World Wide Web, pages 897–908, 2013

    2013

  19. [27]

    Correcting false alarms from unseen: Adapting graph anomaly detectors at test time

    Junjun Pan, Yixin Liu, Chuan Zhou, Fei Xiong, Alan Wee-Chung Liew, and Shirui Pan. Correcting false alarms from unseen: Adapting graph anomaly detectors at test time. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 24691–24699, 2026. 13 Detect...

    2026

  20. [28]

    Benchmarking agentic workflow generation

    Shuofei Qiao, Runnan Fang, Zhisong Qiu, Xiaobin Wang, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, and Huajun Chen. Benchmarking agentic workflow generation. InInternational Conference on Learning Representations, volume 2025, pages 69679–69703, 2025

    2025

  21. [29]

    Collective opinion spam detection: Bridging review networks and metadata

    Shebuti Rayana and Leman Akoglu. Collective opinion spam detection: Bridging review networks and metadata. InProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 985–994, 2015

    2015

  22. [30]

    Heterogeneous graph neural network with multi-view representation learning.IEEE Transactions on Knowledge and Data Engineering, 35:11476–11488, 2022

    Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, and Feida Zhu. Heterogeneous graph neural network with multi-view representation learning.IEEE Transactions on Knowledge and Data Engineering, 35:11476–11488, 2022

    2022

  23. [31]

    H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections

    Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, and Jia Wu. H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections. InProceedings of the World Wide Web, pages 1486–1494, 2022

    2022

  24. [32]

    Kronos: A foundation model for the language of financial markets

    Yu Shi, Zongliang Fu, Shuo Chen, Bohan Zhao, Wei Xu, Changshui Zhang, and Jian Li. Kronos: A foundation model for the language of financial markets. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 25366–25373, 2026

    2026

  25. [33]

    Uniform: Towards unified framework for anomaly detection on graphs

    Chuancheng Song, Xixun Lin, Hanyang Shen, Yanmin Shang, and Yanan Cao. Uniform: Towards unified framework for anomaly detection on graphs. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 12559–12567, 2025

    2025

  26. [34]

    Mitigating message imbalance in fraud detection with dual-view graph representation learning

    Yudan Song, Yuecen Wei, Yuhang Lu, Qingyun Sun, Minglai Shao, Li-e Wang, Chunming Hu, Xianxian Li, and Xingcheng Fu. Mitigating message imbalance in fraud detection with dual-view graph representation learning. InProceedings of the International Joint Conference on Artificial ...

    2025

  27. [35]

    Anomaly subgraph detection through high-order sampling contrastive learning

    Ying Sun, Wenjun Wang, Nannan Wu, and Chunlong Bao. Anomaly subgraph detection through high-order sampling contrastive learning. InProceedings of the International Joint Conference on Artificial Intelligence, pages 2362–2369, 2024

    2024

  28. [36]

    Rethinking graph neural networks for anomaly detection

    Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li. Rethinking graph neural networks for anomaly detection. InInternational Conference on Machine Learning, pages 21076–21089, 2022

    2022

  29. [37]

    Sad: semi-supervised anomaly detection on dynamic graphs

    ShengTian,JihaiDong,JintangLi,WenlongZhao,XiaolongXu,BaokunWang,BowenSong,Changhua Meng, Tianyi Zhang, and Liang Chen. Sad: semi-supervised anomaly detection on dynamic graphs. InProceedings of the International Joint Conference on Artificial Intelligence, pages 2306–2314, 2023

    2023

  30. [38]

    Mlp-mixer: An all-mlp architec- ture for vision.Advances in Neural Information Processing Systems, 34(2):24261–24272, 2021

    Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. Mlp-mixer: An all-mlp architec- ture for vision.Advances in Neural Information Processing Systems, 34(2)...

    2021

  31. [39]

    Graph attention networks

    Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. InInternational Conference on Learning Representations, pages 276–289, 2018

    2018

  32. [40]

    Attention-based conditional random field for financial fraud detection

    Xiaoguang Wang, Chenxu Wang, Luyue Zhang, Xiaole Wang, Mengqin Wang, Huanlong Liu, and Tao Qin. Attention-based conditional random field for financial fraud detection. InProceedings of the International Joint Conference on Artificial Intelligence, pages 7822–7830, 2025. 14 Det...

    2025

  33. [41]

    Dyflow: Dynamic workflow framework for agentic reasoning

    Yanbo Wang, Zixiang Xu, Yue Huang, Xiangqi Wang, Zirui Song, Lang Gao, Chenxi Wang, Robert Tang, Yue Zhao, Arman Cohan, et al. Dyflow: Dynamic workflow framework for agentic reasoning. Advances in Neural Information Processing Systems, 38:174148–174181, 2026

    2026

  34. [42]

    Simple and efficient heterogeneous temporal graph neural network.Advances in Neural Information Processing Systems, 2025

    Yili Wang, Tairan Huang, Changlong He, Qiutong Li, and Jianliang Gao. Simple and efficient heterogeneous temporal graph neural network.Advances in Neural Information Processing Systems, 2025

    2025

  35. [43]

    Evoagentx: An automated framework for evolving agentic workflows

    Yingxu Wang, Siwei Liu, Jinyuan Fang, and Zaiqiao Meng. Evoagentx: An automated framework for evolving agentic workflows. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 643–655, 2025

    2025

  36. [44]

    Label information enhanced fraud detection against low homophily in graphs

    Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, et al. Label information enhanced fraud detection against low homophily in graphs. InProceedings of the World Wide Web, pages 406–416, 2023

    2023

  37. [45]

    Cats: cross-platform e-commerce fraud detection

    Haiqin Weng, Shouling Ji, Fuzheng Duan, Zhao Li, Jianhai Chen, Qinming He, and Ting Wang. Cats: cross-platform e-commerce fraud detection. InProceedings of IEEE International Conference on Data Engineering, pages 1874–1885, 2019

    2019

  38. [46]

    Gif: A general graph unlearning strategy via influence function

    Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, and Xiangnan He. Gif: A general graph unlearning strategy via influence function. InProceedings of the ACM Web Conference, pages 651–661, 2023

    2023

  39. [47]

    Deltagrad: Rapid retraining of machine learning models

    Yinjun Wu, Edgar Dobriban, and Susan Davidson. Deltagrad: Rapid retraining of machine learning models. InInternational Conference on Machine Learning, pages 10355–10366, 2020

    2020

  40. [48]

    Howpowerfularegraphneuralnetworks? InInternational Conference on Learning Representations, pages 215–224, 2019

    KeyuluXu, WeihuaHu, JureLeskovec, andStefanieJegelka. Howpowerfularegraphneuralnetworks? InInternational Conference on Learning Representations, pages 215–224, 2019

    2019

  41. [49]

    Geogen: A two-stage coarse-to-fineframeworkforfine-grainedsyntheticlocation-basedsocialnetworktrajectorygeneration

    Rongchao Xu, Kunlin Cai, Lin Jiang, Zhiqing Hong, Yuan Tian, and Guang Wang. Geogen: A two-stage coarse-to-fineframeworkforfine-grainedsyntheticlocation-basedsocialnetworktrajectorygeneration. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 13...

    2026

  42. [50]

    Grad: Guided relation diffusion generation for graph augmentation in graph fraud detection

    Jie Yang, Rui Zhang, Ziyang Cheng, Dawei Cheng, Guang Yang, and Bo Wang. Grad: Guided relation diffusion generation for graph augmentation in graph fraud detection. InProceedings of the ACM on Web Conference, pages 5308–5319, 2025

    2025

  43. [51]

    Sfga: Similarity-constrained fusion learning for unsu- pervised anomaly detection in multiplex graphs

    Huiliang Zhai, Xiangyi Teng, and Jing Liu. Sfga: Similarity-constrained fusion learning for unsu- pervised anomaly detection in multiplex graphs. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 28140–28149, 2026

    2026

  44. [52]

    Aflow: Automating agentic workflow generation

    Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, et al. Aflow: Automating agentic workflow generation. In International Conference on Learning Representations, volume 2025, pages 34040–34077, 2025

    2025

  45. [53]

    Dig-in-gnn: Discriminative feature guided gnn-based fraud detector against inconsistencies in multi-relation fraud graph

    Jinghui Zhang, Zhengjia Xu, Dingyang Lv, Zhan Shi, Dian Shen, Jiahui Jin, and Fang Dong. Dig-in-gnn: Discriminative feature guided gnn-based fraud detector against inconsistencies in multi-relation fraud graph. InProceedings of the AAAI Conference on Artificial Intelligence, p...

    2024

  46. [54]

    Multi-domain deep learning from a multi-view perspective for cross-border e-commerce search

    Yiqian Zhang, Yinfu Feng, Wen-Ji Zhou, Yunan Ye, Min Tan, Rong Xiao, Haihong Tang, Jiajun Ding, and Jun Yu. Multi-domain deep learning from a multi-view perspective for cross-border e-commerce search. InProceedings of the AAAI Conference on Artificial Intelligence, volume 38, ...

    2024

  47. [55]

    Gnns as predictors of agentic workflow performances.arXiv preprint arXiv:2503.11301, 2025

    Yuanshuo Zhang, Yuchen Hou, Bohan Tang, Shuo Chen, Muhan Zhang, Xiaowen Dong, and Siheng Chen. Gnns as predictors of agentic workflow performances.arXiv preprint arXiv:2503.11301, 2025

    2025

  48. [56]

    Partitioning message passing for graph fraud detection

    Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, and Jia Chen. Partitioning message passing for graph fraud detection. InInternational Conference on Learning Representations, pages 120–129, 2024. 16 Detect by Yourself: Self-Designing Agentic Workflows f...

    2024

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.