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Paper Citation Record · LEDGER

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection

As of 7 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2605.26446.

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

pith.paper-citation-record.v1
2605.26446 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:59:17.787145Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

13 of 13 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0326bcbe-590e-4a16-81f3-d6fe84305c13 · outbound

This paper cites Graph based anomaly detection and description: A survey,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Graph based anomaly detection and description: A survey,

Reference 1

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:22b9adbd75e279499204fc57018cb59e10e849f1b67b63bcd36008329adc9892

Observation f432e6ae-f0cb-4c25-8ba7-4d45d1cb2f4f · outbound

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

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Semi-supervised classification with graph convolutional networks,

Reference 2

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:d1b07bbc1d98c52b2701d5a45e547003574f6d2dff11f2f66873c9723cf070be

Observation 04865f16-e69d-4140-bfd3-48cfa8a84270 · outbound

This paper cites Denoising diffusion probabilistic models,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Denoising diffusion probabilistic models,

Reference 3

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:d8b71fe39c14f19e1f9fe2039b963756c35220b6d40e1a4c13d0b89f8bea3b32

Observation a7e22937-d424-444d-ae89-22a54402d685 · outbound

This paper cites Inductive representation learning on large graphs,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Inductive representation learning on large graphs,

Reference 4

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:deafcf08c80f3991063e8c37446189c242315dbe68a5bf6810fb9d643b8236d1

Observation 5a4b4532-3272-4942-8056-e1ab9ff7873c · outbound

This paper cites Graph attention networks,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Graph attention networks,

Reference 5

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:954c8b9142a67e211f85a75c3cff490b8cbb1da8405aa2f3089ec2d61a242453

Observation 42d4a24a-bd6e-4bd3-8794-a7b0531db3a7 · outbound

This paper cites Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-06-29T20:03:56.262627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:d05826cebb9ee334939ea6cecc720573c40398558dd4eeb03b5aca407fea9893

Observation bd1e3bd0-e423-4a38-af0f-e5c98a899477 · outbound

This paper cites Tranad: Deep transformer networks for anomaly detection in multivariate time series data,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Tranad: Deep transformer networks for anomaly detection in multivariate time series data,

Reference 7

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:440dd06fd8150177207efe31de16b75e71823df8752dfb68b4d726658b5d2539

Observation 6115497c-122a-4cd3-b9bf-0bbff81cba0b · outbound

This paper cites Diffgad: A diffusion-based unsupervised graph anomaly detector,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Diffgad: A diffusion-based unsupervised graph anomaly detector,

Reference 8

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:53b2bd445cc21ab79101049984bafdf37f4964a84f573dbc7925ea1ee48ac78a

Observation ec8e89a9-9e2d-4920-9289-36f31e5cda0e · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 9

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:f191e113ceabc3e49192b0f921bc7feb648a1a0d468d2c5b8fb1631f78e5a204

Observation 08d21450-ff0f-4cc9-8194-5de93ef464fc · outbound

This paper cites Deep anomaly detection on attributed networks,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Deep anomaly detection on attributed networks,

Reference 10

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:63eb70817316ba1860a1d1c49056f6f6645b08e08e38e952eecda554808f0744

Observation ccc771ec-5c23-4fc3-944c-97c6bfa663af · outbound

This paper cites Anomalydae: Dual autoencoder for anomaly detection on attributed networks,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Anomalydae: Dual autoencoder for anomaly detection on attributed networks,

Reference 11

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:69b1271daf72d55948fbc5af770f9f1b7181523c6d905b4b53542316390431b6

Observation df06332e-4d6b-4ed5-a5d7-f10730a2a25d · outbound

This paper cites Anomaly detection on attributed networks via contrastive self- supervised learning,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Anomaly detection on attributed networks via contrastive self- supervised learning,

Reference 12

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:9f0cb8d6f154f27219e43b8bd8e6e2f2aa821176aeca6c354f572dfacdd4c91f

Observation 0bc3b6de-b103-403b-8979-414a74306478 · outbound

This paper cites Graphmae: Self-supervised masked graph autoencoders,.

DDGAD: Trajectory Dynamics for Diffusion-Based Graph Anomaly Detection Graphmae: Self-supervised masked graph autoencoders,

Reference 13

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unresolved
no resolver link, observed 2026-06-29T19:59:17.787145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T19:59:17.787145Z digest=sha256:3e0d983e2f0e48999176545941aa62a9166b376356610c426fa0018628391e3d

Pith citing papers

No inbound Pith citation observations are available.