Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection | Paper Citation Record

Observation 15152634-9bda-499f-9154-76da6a475122 · outbound

This paper cites Optimizing agentic workflows using meta-tools.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Optimizing agentic workflows using meta-tools

Reference 1

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Semi-supervised graph anomaly detection via robust homophily learning.Advances in Neural Information Processing Systems, 38:133988–134013, 2026

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This paper cites Geoflow: Agentic workflow automation for geospatial tasks.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Geoflow: Agentic workflow automation for geospatial tasks

Reference 3

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection How attentive are graph attention networks? InInternational Conference on Learning Representations, pages 1315–1324, 2022

Reference 4

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Observation 1237a6b5-50cc-4a50-ba4d-433e7b063eb9 · outbound

This paper cites Towards multiple missing values- resistant unsupervised graph anomaly detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Towards multiple missing values- resistant unsupervised graph anomaly detection

Reference 5

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Observation b57d71c2-5147-4d8e-a0c1-1ad9f3176e20 · outbound

This paper cites Consistency training with learnable data augmentation for graph anomaly detection with limited supervision.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Consistency training with learnable data augmentation for graph anomaly detection with limited supervision

Reference 6

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This paper cites Agent4edu: Advancing ai for education with agentic workflows.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Agent4edu: Advancing ai for education with agentic workflows

Reference 7

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Spacegnn: Multi-space graph neural network for node anomaly detection with extremely limited labels

Reference 8

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Observation 4e01e6d1-03e9-4acb-a5ea-cae9dabf23c9 · outbound

This paper cites Enhancing graph neural network-based fraud detectors against camouflaged fraudsters.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Enhancing graph neural network-based fraud detectors against camouflaged fraudsters

Reference 9

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Observation b2e6d76d-11dc-4be7-b1b9-c37fd1ee49ad · outbound

This paper cites Alleviating structural distribution shift in graph anomaly detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Alleviating structural distribution shift in graph anomaly detection

Reference 10

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Observation 47f1a5c8-70bd-4a76-a595-e45a6fa779b3 · outbound

This paper cites Addressing heterophily in graph anomaly detection: A perspective of graph spectrum.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Addressing heterophily in graph anomaly detection: A perspective of graph spectrum

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Observation 0b1d5d4b-6ca9-4c52-bf71-54bd4f166288 · outbound

This paper cites Naast-gnn: Neighborhood adaptive aggregation and spectral tuning for graph anomaly detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Naast-gnn: Neighborhood adaptive aggregation and spectral tuning for graph anomaly detection

Reference 12

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Observation efaa1dd2-5f20-4207-8024-5ec91f1e06bf · outbound

This paper cites Topological anomaly quantification for semi-supervised graph anomaly detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Topological anomaly quantification for semi-supervised graph anomaly detection

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Observation 7bf60e39-2945-42c8-b44b-717625472ab2 · outbound

This paper cites Inductive representation learning on large graphs.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Inductive representation learning on large graphs

Reference 14

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This paper cites Tdflow: Agentic workflows for test driven development.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Tdflow: Agentic workflows for test driven development

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This paper cites Tempasd: Temporal anomalous subgraph discovery in large-scale dynamic financial networks.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Tempasd: Temporal anomalous subgraph discovery in large-scale dynamic financial networks

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Observation 0bc1961d-512d-49b2-8c67-c456fce83320 · outbound

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Revisiting low- homophily for graph-based fraud detection.Neural Networks, 188:107407, 2025

Reference 17

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Observation ed002a6d-bde3-4870-8104-bddc2b3762fe · outbound

This paper cites Can llms find fraudsters? multi-level llm enhanced graph fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Can llms find fraudsters? multi-level llm enhanced graph fraud detection

Reference 18

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Observation 84498b2b-6224-4723-b9d8-5b785a8258a5 · outbound

This paper cites Rethinking reconstruction-based graph-level anomaly detection: limitations and a simple remedy.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Rethinking reconstruction-based graph-level anomaly detection: limitations and a simple remedy

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Observation 6f85b480-6dcb-4e14-9d7c-7ce6be527e90 · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Semi-supervised classification with graph convolutional networks

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This paper cites Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning

Reference 21

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Observation 9dad7b7f-4094-4b5a-8568-e3d3daacb9b6 · outbound

This paper cites Dual-augment graph neural network for fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Dual-augment graph neural network for fraud detection

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Observation 7a0748fe-6b01-4567-b634-5b3aff561bb6 · outbound

This paper cites Pick and choose: a gnn-based imbalanced learning approach for fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Pick and choose: a gnn-based imbalanced learning approach for fraud detection

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Observation dd9dfc26-098b-4ec8-b3e9-4c5dcd1ffea0 · outbound

This paper cites Multi-dimensional adaptive mix-hop contextual learning framework for universal graph anomaly detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Multi-dimensional adaptive mix-hop contextual learning framework for universal graph anomaly detection

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Observation 628bd421-fbe7-425f-8113-f05eb02b8e38 · outbound

This paper cites Alleviating the inconsistency problem of applying graph neural network to fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Alleviating the inconsistency problem of applying graph neural network to fraud detection

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This paper cites From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews

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This paper cites Correcting false alarms from unseen: Adapting graph anomaly detectors at test time.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Correcting false alarms from unseen: Adapting graph anomaly detectors at test time

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This paper cites Benchmarking agentic workflow generation.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Benchmarking agentic workflow generation

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Observation cd1822d8-f555-4866-8cda-55f5e53c0c89 · outbound

This paper cites Collective opinion spam detection: Bridging review networks and metadata.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Collective opinion spam detection: Bridging review networks and metadata

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Heterogeneous graph neural network with multi-view representation learning.IEEE Transactions on Knowledge and Data Engineering, 35:11476–11488, 2022

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Observation 1976c9f9-8131-44ef-b946-b00f6bd3e497 · outbound

This paper cites H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections

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This paper cites Kronos: A foundation model for the language of financial markets.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Kronos: A foundation model for the language of financial markets

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Observation 2d12c881-6cec-4cdf-9de0-b05b98972311 · outbound

This paper cites Uniform: Towards unified framework for anomaly detection on graphs.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Uniform: Towards unified framework for anomaly detection on graphs

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Observation b9b2a302-c4a5-4c4f-883f-3e2513b76d00 · outbound

This paper cites Mitigating message imbalance in fraud detection with dual-view graph representation learning.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Mitigating message imbalance in fraud detection with dual-view graph representation learning

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Observation 5c9f23f4-3754-4d73-9152-54e3aca3989b · outbound

This paper cites Anomaly subgraph detection through high-order sampling contrastive learning.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Anomaly subgraph detection through high-order sampling contrastive learning

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Observation 7371b13f-2223-4295-bc59-9b378f5355e9 · outbound

This paper cites Rethinking graph neural networks for anomaly detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Rethinking graph neural networks for anomaly detection

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Observation 6321e3db-8faa-48d6-ba78-ad3d4be505ae · outbound

This paper cites Sad: semi-supervised anomaly detection on dynamic graphs.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Sad: semi-supervised anomaly detection on dynamic graphs

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Observation 0b96dc2c-d4c3-496b-b0fd-96a8f7a87343 · outbound

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Mlp-mixer: An all-mlp architec- ture for vision.Advances in Neural Information Processing Systems, 34(2):24261–24272, 2021

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Observation 2c75fa39-f73c-4a8c-b085-44caa044c7dc · outbound

This paper cites Graph attention networks.

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

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Observation 8bd96151-52c2-41ac-b59a-c7e31739c867 · outbound

This paper cites Attention-based conditional random field for financial fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Attention-based conditional random field for financial fraud detection

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Observation 3e4ccb1b-2f46-4f0c-9ab1-41e3c87499f1 · outbound

This paper cites Dyflow: Dynamic workflow framework for agentic reasoning.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Dyflow: Dynamic workflow framework for agentic reasoning

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Observation 3e9bda9c-84e7-47f4-a1fe-7619d83f54cb · outbound

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Simple and efficient heterogeneous temporal graph neural network.Advances in Neural Information Processing Systems, 2025

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This paper cites Evoagentx: An automated framework for evolving agentic workflows.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Evoagentx: An automated framework for evolving agentic workflows

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Observation d4edda25-6f4f-4138-bc9a-71d6d363bede · outbound

This paper cites Label information enhanced fraud detection against low homophily in graphs.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Label information enhanced fraud detection against low homophily in graphs

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Observation 5d0e6163-6c56-438a-8773-2195500ebd1b · outbound

This paper cites Cats: cross-platform e-commerce fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Cats: cross-platform e-commerce fraud detection

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Observation 6e3cf969-e36c-4d13-9ab3-c59af26cb632 · outbound

This paper cites Gif: A general graph unlearning strategy via influence function.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Gif: A general graph unlearning strategy via influence function

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This paper cites Deltagrad: Rapid retraining of machine learning models.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Deltagrad: Rapid retraining of machine learning models

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This paper cites Howpowerfularegraphneuralnetworks? InInternational Conference on Learning Representations, pages 215–224, 2019.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Howpowerfularegraphneuralnetworks? InInternational Conference on Learning Representations, pages 215–224, 2019

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Observation 306cde92-5e1c-4865-9eda-2714eeef1a9d · outbound

This paper cites Geogen: A two-stage coarse-to-fineframeworkforfine-grainedsyntheticlocation-basedsocialnetworktrajectorygeneration.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Geogen: A two-stage coarse-to-fineframeworkforfine-grainedsyntheticlocation-basedsocialnetworktrajectorygeneration

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This paper cites Grad: Guided relation diffusion generation for graph augmentation in graph fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Grad: Guided relation diffusion generation for graph augmentation in graph fraud detection

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Observation 8a866761-5b9a-4e51-a809-2d57470273b8 · outbound

This paper cites Sfga: Similarity-constrained fusion learning for unsu- pervised anomaly detection in multiplex graphs.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Sfga: Similarity-constrained fusion learning for unsu- pervised anomaly detection in multiplex graphs

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This paper cites Aflow: Automating agentic workflow generation.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Aflow: Automating agentic workflow generation

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Observation f2b2878c-f49f-43cc-aa3a-755d5279d816 · outbound

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Dig-in-gnn: Discriminative feature guided gnn-based fraud detector against inconsistencies in multi-relation fraud graph

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

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Multi-domain deep learning from a multi-view perspective for cross-border e-commerce search

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This paper cites GNNs as Predictors of Agentic Workflow Performances.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection GNNs as Predictors of Agentic Workflow Performances

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This paper cites Partitioning message passing for graph fraud detection.

Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection Partitioning message passing for graph fraud detection

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