Paper Citation Record · LEDGER

Normality Calibration in Semi-supervised Graph Anomaly Detection

As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2510.02014.

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

pith.paper-citation-record.v1
2510.02014 v3

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T12:48:09.757460Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4031733d-72e8-47bf-8187-2146473587c1 · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

Normality Calibration in Semi-supervised Graph Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:07.552118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7c7b4d7-e1d4-457e-9041-2da781f1e2bd · outbound

This paper cites Semi-supervised and un- supervised deep visual learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 46(3):1327–1347, 2022b.

Normality Calibration in Semi-supervised Graph Anomaly Detection Semi-supervised and un- supervised deep visual learning: A survey.IEEE transactions on pattern analysis and machine intelligence, 46(3):1327–1347, 2022b

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:07.906119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 416b6b02-ee50-4111-84ea-9d5910828e42 · outbound

This paper cites • Amazon (Dou et al., 2020): It is a co-review network obtained from the Musical Instrument category on Amazon.com.

Normality Calibration in Semi-supervised Graph Anomaly Detection • Amazon (Dou et al., 2020): It is a co-review network obtained from the Musical Instrument category on Amazon.com

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:09.451408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1206bba-addc-4c57-b5d3-3845564ffef7 · outbound

This paper cites Ad- dressing heterophily in graph anomaly detection: A perspective of graph spectrum.

Normality Calibration in Semi-supervised Graph Anomaly Detection Ad- dressing heterophily in graph anomaly detection: A perspective of graph spectrum

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.411761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afad383f-9eb4-4481-860e-d644d577ebbb · outbound

This paper cites InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning.

Normality Calibration in Semi-supervised Graph Anomaly Detection InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.587344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0017d382-1903-4f42-b780-f88cde62e8e8 · outbound

This paper cites A review of pseudo- labeling for computer vision.arXiv preprint arXiv:2408.07221,.

Normality Calibration in Semi-supervised Graph Anomaly Detection A review of pseudo- labeling for computer vision.arXiv preprint arXiv:2408.07221,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.679182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 873b476e-12d9-46be-b8cf-0df98ce0460e · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Normality Calibration in Semi-supervised Graph Anomaly Detection Pitfalls of Graph Neural Network Evaluation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:09.184279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50b63952-ac8d-418e-a27d-b414f2eda1cb · outbound

This paper cites The local node affinity is calculated on the learned representations on the truncated graphs.

Normality Calibration in Semi-supervised Graph Anomaly Detection The local node affinity is calculated on the learned representations on the truncated graphs

Reference 15

Resolution: malformed identifier; no resolver link, observed 2026-08-04T12:48:09.637457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d18cb41f-c770-4746-9c6c-c68efae54a69 · outbound

This paper cites Metric Method Amazon T-Finance Reddit YelpChi Tolokers Photo Avg.

Normality Calibration in Semi-supervised Graph Anomaly Detection Metric Method Amazon T-Finance Reddit YelpChi Tolokers Photo Avg

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:09.757460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e6feea5-df4b-4690-b0ab-e600f7b82a52 · outbound

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

Normality Calibration in Semi-supervised Graph Anomaly Detection Pick and choose: a gnn-based imbalanced learning approach for fraud detection

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.891150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9af77c52-f35d-4513-bcc5-fffb398691c8 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Normality Calibration in Semi-supervised Graph Anomaly Detection Temporal Ensembling for Semi-Supervised Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.785109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a3b1af3-a66f-431d-9ef2-25e40d847849 · outbound

This paper cites Deep anomaly detection on attributed networks.

Normality Calibration in Semi-supervised Graph Anomaly Detection Deep anomaly detection on attributed networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.113199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feb4a9b7-f00e-4363-8b27-fc999ea4d478 · outbound

This paper cites Deep Learning for Video Anomaly Detection: A Review.

Normality Calibration in Semi-supervised Graph Anomaly Detection Deep Learning for Video Anomaly Detection: A Review

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:09.310467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eac8c0dc-07c3-4e9a-b193-8c6e95e2db9d · outbound

This paper cites Anomalydae: Dual autoencoder for anomaly detec- tion on attributed networks.

Normality Calibration in Semi-supervised Graph Anomaly Detection Anomalydae: Dual autoencoder for anomaly detec- tion on attributed networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:08.242021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ce14a2d-dea3-4ad7-82be-09aa60af95cc · outbound

This paper cites SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning.

Normality Calibration in Semi-supervised Graph Anomaly Detection SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:07.716713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59462642-ebca-43b4-a0cd-dec39a56670d · outbound

This paper cites Deep Semi-Supervised Anomaly Detection.

Normality Calibration in Semi-supervised Graph Anomaly Detection Deep Semi-Supervised Anomaly Detection

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:09.036994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.