Paper Citation Record · LEDGER

Towards Anomaly Detection on Relational Data

As of 22 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2606.18621.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.18621 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T04:43:26.228527Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:39:39.224102Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

60 of 60 outbound references displayed

verified exact0
verified fuzzy0
unresolved59
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation adb6317b-98ca-4132-9917-c9480dab4589 · outbound

This paper cites Relbench: A benchmark for deep learning on relational databases.Advances in Neural Information Processing Systems, 37:21330–21341, 2024.

Towards Anomaly Detection on Relational Data Relbench: A benchmark for deep learning on relational databases.Advances in Neural Information Processing Systems, 37:21330–21341, 2024

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:22.582296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55fbce9b-fa89-4eeb-987b-aa3f8fec34ff · outbound

This paper cites Relational deep learning: Challenges, foundations and next-generation architectures.

Towards Anomaly Detection on Relational Data Relational deep learning: Challenges, foundations and next-generation architectures

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:22.739721Z

Source-reported events for the cited work

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Observation 1b166379-d473-487a-a424-14daef9a6343 · outbound

This paper cites Relational graph transformer.

Towards Anomaly Detection on Relational Data Relational graph transformer

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:22.803535Z

Source-reported events for the cited work

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Observation b1e8fdf5-b568-4362-994e-7ad8d72c6b9b · outbound

This paper cites Relgnn: Composite message passing for relational deep learning.

Towards Anomaly Detection on Relational Data Relgnn: Composite message passing for relational deep learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:22.923833Z

Source-reported events for the cited work

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Observation 83ba30a5-803b-49db-962c-32e04da9dbaf · outbound

This paper cites Relational transformer: Toward zero-shot foundation models for relational data.

Towards Anomaly Detection on Relational Data Relational transformer: Toward zero-shot foundation models for relational data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:22.983431Z

Source-reported events for the cited work

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Observation a876d2a1-3eb9-4de0-b678-923684e1c080 · outbound

This paper cites Griffin: Towards a graph-centric relational database foundation model.

Towards Anomaly Detection on Relational Data Griffin: Towards a graph-centric relational database foundation model

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.144740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 495e9b1d-7861-42ee-98a8-5dcf4110c5a2 · outbound

This paper cites Beyond individual input for deep anomaly detection on tabular data.

Towards Anomaly Detection on Relational Data Beyond individual input for deep anomaly detection on tabular data

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.208517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a3d373d-52bd-412d-9f4c-10b22f6e530b · outbound

This paper cites Drl: Decomposed representation learning for tabular anomaly detection.

Towards Anomaly Detection on Relational Data Drl: Decomposed representation learning for tabular anomaly detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.324748Z

Source-reported events for the cited work

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Observation 2a6000c2-0420-462e-b30b-6791bec4cd74 · outbound

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

Towards Anomaly Detection on Relational Data Correcting false alarms from unseen: Adapting graph anomaly detectors at test time

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.422480Z

Source-reported events for the cited work

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Observation 16e5899c-6159-48b1-91cf-8a68446df039 · outbound

This paper cites A survey of generalization of graph anomaly detection: From transfer learning to foundation models.

Towards Anomaly Detection on Relational Data A survey of generalization of graph anomaly detection: From transfer learning to foundation models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.462966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7df1e24d-8087-478c-b8b7-db8681e06133 · outbound

This paper cites Deep anomaly detection on attributed networks.

Towards Anomaly Detection on Relational Data Deep anomaly detection on attributed networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.544904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e29debee-a9f7-4329-9dbe-d9088ece3a0f · outbound

This paper cites Anomaly detection on attributed networks via contrastive self-supervised learning.IEEE transactions on neural networks and learning systems, 33(6):2378–2392, 2021.

Towards Anomaly Detection on Relational Data Anomaly detection on attributed networks via contrastive self-supervised learning.IEEE transactions on neural networks and learning systems, 33(6):2378–2392, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.603646Z

Source-reported events for the cited work

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Observation c5359ab5-02d9-4fda-a513-0d4ea701ab31 · outbound

This paper cites Boosting graph anomaly detection with adaptive message passing.

Towards Anomaly Detection on Relational Data Boosting graph anomaly detection with adaptive message passing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.697886Z

Source-reported events for the cited work

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Observation edc82f39-d6c4-4e4d-856a-3c369017f987 · outbound

This paper cites Camera: Adapting to semantic camouflage in unsupervised text-attributed graph fraud detection.

Towards Anomaly Detection on Relational Data Camera: Adapting to semantic camouflage in unsupervised text-attributed graph fraud detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.740258Z

Source-reported events for the cited work

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Observation 74d2793a-2d37-46a6-8aaf-1fd2ae67e319 · outbound

This paper cites Lof: identifying density-based local outliers.

Towards Anomaly Detection on Relational Data Lof: identifying density-based local outliers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.794514Z

Source-reported events for the cited work

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Observation 362bcf99-67b9-4ab3-8e46-449d3ddeed26 · outbound

This paper cites Lunar: Unifying local outlier detection methods via graph neural networks.

Towards Anomaly Detection on Relational Data Lunar: Unifying local outlier detection methods via graph neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.839230Z

Source-reported events for the cited work

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Observation 39fccdf4-6874-4bc0-92ca-3ccec74c7af9 · outbound

This paper cites Mcm: Masked cell modeling for anomaly detection in tabular data.

Towards Anomaly Detection on Relational Data Mcm: Masked cell modeling for anomaly detection in tabular data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:23.936286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe63a8c0-e0d2-438a-9263-ed98cc9063b5 · outbound

This paper cites Uncertainty- aware graph neural networks: A multihop evidence fusion approach.IEEE Transactions on Neural Networks and Learning Systems, 2025.

Towards Anomaly Detection on Relational Data Uncertainty- aware graph neural networks: A multihop evidence fusion approach.IEEE Transactions on Neural Networks and Learning Systems, 2025

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.017962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1cbe950-ee58-4408-9f7a-050ba14b2efa · outbound

This paper cites Influence-oriented Personalized Federated Learning.

Towards Anomaly Detection on Relational Data Influence-oriented Personalized Federated Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.105594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77c7bffc-d7b5-4e5e-b6e5-e1792f005596 · outbound

This paper cites Arc: A generalist graph anomaly detector with in-context learning.Advances in Neural Information Processing Systems, 37:50772–50804, 2024.

Towards Anomaly Detection on Relational Data Arc: A generalist graph anomaly detector with in-context learning.Advances in Neural Information Processing Systems, 37:50772–50804, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.148631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 361c1382-ca1c-4024-823b-f6e81ac51144 · outbound

This paper cites Freegad: A training- free yet effective approach for graph anomaly detection.

Towards Anomaly Detection on Relational Data Freegad: A training- free yet effective approach for graph anomaly detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.249332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53fe5b1d-4e0d-4d74-9dad-9b3dbf2a3112 · outbound

This paper cites RelBench v2: A Large-Scale Benchmark and Repository for Relational Data.

Towards Anomaly Detection on Relational Data RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.399312Z

Source-reported events for the cited work

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Observation 012e5ed2-fb78-4e2f-b28a-7c82e9762416 · outbound

This paper cites Salt: Sales autocompletion linked business tables dataset.

Towards Anomaly Detection on Relational Data Salt: Sales autocompletion linked business tables dataset

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.491016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d68973d2-b3ac-43cc-abeb-b74831b3e654 · outbound

This paper cites Prem: A simple yet effective approach for node- level graph anomaly detection.

Towards Anomaly Detection on Relational Data Prem: A simple yet effective approach for node- level graph anomaly detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.558802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4175c13-41d6-4804-81b5-ebbfd78ccc33 · outbound

This paper cites Efficient algorithms for mining outliers from large data sets.

Towards Anomaly Detection on Relational Data Efficient algorithms for mining outliers from large data sets

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.581074Z

Source-reported events for the cited work

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Observation 56a76c19-2316-46e0-adf3-00f91338ff77 · outbound

This paper cites Generative adversarial active learning for unsupervised outlier detection.IEEE Transactions on Knowledge and Data Engineering, 32(8):1517–1528, 2019.

Towards Anomaly Detection on Relational Data Generative adversarial active learning for unsupervised outlier detection.IEEE Transactions on Knowledge and Data Engineering, 32(8):1517–1528, 2019

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.659301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2460599-587f-4527-94b3-92d051a6c520 · outbound

This paper cites Relational deep learning: Graph representation learning on relational databases.

Towards Anomaly Detection on Relational Data Relational deep learning: Graph representation learning on relational databases

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.758573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9464aa81-d9a5-4734-ac61-020964937501 · outbound

This paper cites Large language models are good relational learners.

Towards Anomaly Detection on Relational Data Large language models are good relational learners

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.848445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9d39e95-6d17-4581-a286-2a1d71daaf42 · outbound

This paper cites PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models.

Towards Anomaly Detection on Relational Data PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.917663Z

Source-reported events for the cited work

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Observation d63f4417-363a-42f1-bfc3-b0375f096acc · outbound

This paper cites Deep learning for anomaly detection: A review.ACM computing surveys (CSUR), 54(2):1–38, 2021.

Towards Anomaly Detection on Relational Data Deep learning for anomaly detection: A review.ACM computing surveys (CSUR), 54(2):1–38, 2021

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:24.956496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1156665-c886-41ef-b047-dff796682f17 · outbound

This paper cites Deep neural networks and tabular data: A survey.IEEE transactions on neural networks and learning systems, 35(6):7499–7519, 2022.

Towards Anomaly Detection on Relational Data Deep neural networks and tabular data: A survey.IEEE transactions on neural networks and learning systems, 35(6):7499–7519, 2022

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.001541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d99db416-78b8-468c-bd74-3d76b4d49527 · outbound

This paper cites Anomaly detection for tabular data with internal contrastive learning.

Towards Anomaly Detection on Relational Data Anomaly detection for tabular data with internal contrastive learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.044331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c050a562-a12a-450d-a664-7b9057c6f086 · outbound

This paper cites A comprehensive survey on graph anomaly detection with deep learning.IEEE transactions on knowledge and data engineering, 35(12):12012–12038, 2021.

Towards Anomaly Detection on Relational Data A comprehensive survey on graph anomaly detection with deep learning.IEEE transactions on knowledge and data engineering, 35(12):12012–12038, 2021

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.090427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7ddcac7-0764-4025-b229-7834a88152a4 · outbound

This paper cites Deep graph anomaly detection: A survey and new perspectives.IEEE Transactions on Knowledge and Data Engineer- ing, 2025.

Towards Anomaly Detection on Relational Data Deep graph anomaly detection: A survey and new perspectives.IEEE Transactions on Knowledge and Data Engineer- ing, 2025

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.158062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8956886a-4c1d-4da3-9cd9-062ec24d57ed · outbound

This paper cites From few-shot to zero-shot: Towards generalist graph anomaly detection.IEEE Transactions on Knowledge and Data Engineering, 2026.

Towards Anomaly Detection on Relational Data From few-shot to zero-shot: Towards generalist graph anomaly detection.IEEE Transactions on Knowledge and Data Engineering, 2026

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.192153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf2cc1ba-8400-4793-8b42-9b521d088e16 · outbound

This paper cites Raising the bar in graph ood generalization: Invariant learning beyond explicit environment modeling.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026.

Towards Anomaly Detection on Relational Data Raising the bar in graph ood generalization: Invariant learning beyond explicit environment modeling.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.248728Z

Source-reported events for the cited work

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Observation 7544987d-f200-42e5-8475-a17b8b84e53b · outbound

This paper cites Fedcigar: A personalized reconstruction approach for federated graph-level anomaly detection.

Towards Anomaly Detection on Relational Data Fedcigar: A personalized reconstruction approach for federated graph-level anomaly detection

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.289743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8675a1a-0783-4925-8249-72ff0e9dd926 · outbound

This paper cites Rethinking feature alignment in generalist graph anomaly detection: A relational fingerprint-based approach.

Towards Anomaly Detection on Relational Data Rethinking feature alignment in generalist graph anomaly detection: A relational fingerprint-based approach

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.335607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a2c88b8-78f8-4805-aa2b-69b3934ed828 · outbound

This paper cites Anomalous: A joint modeling approach for anomaly detection on attributed networks.

Towards Anomaly Detection on Relational Data Anomalous: A joint modeling approach for anomaly detection on attributed networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.375124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a55886a0-cda7-4b6b-9a72-14dab972d034 · outbound

This paper cites Radar: residual analysis for anomaly detection in attributed networks.

Towards Anomaly Detection on Relational Data Radar: residual analysis for anomaly detection in attributed networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.439198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a5fe4c0-0640-4ec0-85c6-810aede10190 · outbound

This paper cites Truncated affinity maximization: One-class homophily modeling for graph anomaly detection.Advances in Neural Information Processing Systems, 36:49490–49512, 2023.

Towards Anomaly Detection on Relational Data Truncated affinity maximization: One-class homophily modeling for graph anomaly detection.Advances in Neural Information Processing Systems, 36:49490–49512, 2023

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.480579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2df5e5f-8eef-48b3-b811-eee7d1c79dd0 · outbound

This paper cites Modeling relational data with graph convolutional networks.

Towards Anomaly Detection on Relational Data Modeling relational data with graph convolutional networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.524143Z

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Unavailable: canonical work link unavailable.

Observation 3740ace6-237d-44ae-ab12-d77ec7ce1342 · outbound

This paper cites Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks.

Towards Anomaly Detection on Relational Data Blindguard: Safeguarding llm-based multi-agent systems under unknown attacks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.565958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e942836-6eba-4b71-8eb4-0021b1fe95c2 · outbound

This paper cites Dynhd: Hallucination detection for diffusion large language models via denoising dynamics deviation learning.arXiv preprint arXiv:2603.16459, 2026.

Towards Anomaly Detection on Relational Data Dynhd: Hallucination detection for diffusion large language models via denoising dynamics deviation learning.arXiv preprint arXiv:2603.16459, 2026

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.585338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e552c1d7-ee1c-4460-88cc-3a0c76c8b32b · outbound

This paper cites Assemble your crew: Automatic multi-agent communication topology design via autoregressive graph generation.

Towards Anomaly Detection on Relational Data Assemble your crew: Automatic multi-agent communication topology design via autoregressive graph generation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.626596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7a02b19-51e7-4c26-8a3c-fba29aa94473 · outbound

This paper cites Ofa-mas: One-for-all multi-agent system topology design based on mixture-of-experts graph generative models.

Towards Anomaly Detection on Relational Data Ofa-mas: One-for-all multi-agent system topology design based on mixture-of-experts graph generative models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.645623Z

Source-reported events for the cited work

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Observation a211396d-a059-4d9b-8d98-36ee3a0306d5 · outbound

This paper cites Rethinking unsupervised time series anomaly detection: Dynamic attention based on route inverse-masking.Applied Soft Computing, page 113971, 2025.

Towards Anomaly Detection on Relational Data Rethinking unsupervised time series anomaly detection: Dynamic attention based on route inverse-masking.Applied Soft Computing, page 113971, 2025

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.685968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efd87e74-323b-4f27-b822-37809b9195e6 · outbound

This paper cites an unresolved cited work.

Towards Anomaly Detection on Relational Data Unresolved cited work

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.699682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57efa616-9bfc-4d9a-8cbd-fc72bcf45418 · outbound

This paper cites Retrieval augmented deep anomaly detection for tabular data.

Towards Anomaly Detection on Relational Data Retrieval augmented deep anomaly detection for tabular data

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.732028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02a57ef1-0820-426f-b50c-f5d50d26a7cd · outbound

This paper cites Anollm: Large language models for tabular anomaly detection.

Towards Anomaly Detection on Relational Data Anollm: Large language models for tabular anomaly detection

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.766864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62e95787-ae9f-4e93-8ef9-216359799bad · outbound

This paper cites Disentangling tabular data towards better one-class anomaly detection.

Towards Anomaly Detection on Relational Data Disentangling tabular data towards better one-class anomaly detection

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.777888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26665987-034c-4fc2-afce-b5dd088063c4 · outbound

This paper cites Data-efficient and interpretable tabular anomaly detection.

Towards Anomaly Detection on Relational Data Data-efficient and interpretable tabular anomaly detection

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.818927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43965f00-b5e1-48af-9cee-818dfe84d159 · outbound

This paper cites Towards one-for-all anomaly detection for tabular data.

Towards Anomaly Detection on Relational Data Towards one-for-all anomaly detection for tabular data

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.831537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb6eab19-c79d-46ee-a6ff-d5e4e95a6e23 · outbound

This paper cites Graph anomaly detection in time series: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

Towards Anomaly Detection on Relational Data Graph anomaly detection in time series: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.904004Z

Source-reported events for the cited work

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Observation d082e4dd-2ff5-41b8-860a-d5518f3d9ff2 · outbound

This paper cites Address anomalies at critical crossroads for graph anomaly detection.IEEE Transactions on Knowledge and Data Engineering, 2025.

Towards Anomaly Detection on Relational Data Address anomalies at critical crossroads for graph anomaly detection.IEEE Transactions on Knowledge and Data Engineering, 2025

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.951579Z

Source-reported events for the cited work

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Observation 3a66ef70-7532-42c5-9aa7-77ce58e768a9 · outbound

This paper cites Smoothgnn: Smoothing-aware gnn for unsupervised node anomaly detection.

Towards Anomaly Detection on Relational Data Smoothgnn: Smoothing-aware gnn for unsupervised node anomaly detection

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:25.997135Z

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Observation 8dfd2a1b-d970-4f26-ba08-e148739b2149 · outbound

This paper cites verified purchase.

Towards Anomaly Detection on Relational Data verified purchase

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:26.038160Z

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Observation 3353a251-9c40-404b-934e-177662f29f40 · outbound

This paper cites Importantly, anomaly labels are assigned only after verifying that the sampled entities satisfy the dataset-specific injection constraints.

Towards Anomaly Detection on Relational Data Importantly, anomaly labels are assigned only after verifying that the sampled entities satisfy the dataset-specific injection constraints

Reference 58

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Observation 1db29aaa-0377-42da-9cfa-84999cc9c33c · outbound

This paper cites Replace-Only.

Towards Anomaly Detection on Relational Data Replace-Only

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T04:43:26.170723Z

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Observation 4358e7a8-b4db-4874-be66-e67d1fac83d7 · outbound

This paper cites Guided by these principles, we design a separate injection rule for each dataset according to its specific schema and business context.

Towards Anomaly Detection on Relational Data Guided by these principles, we design a separate injection rule for each dataset according to its specific schema and business context

Reference 60

Resolution: malformed identifier; no resolver link, observed 2026-08-04T04:43:26.228527Z

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Pith citing papers

Observation bd62902e-bc32-4ce1-aad5-2c802f31a46e · inbound

CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection cites this paper.

CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection Towards Anomaly Detection on Relational Data

Reference 7

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