Paper Citation Record · LEDGER

Robust Anomaly Detection with Graph Neural Networks using Controllability

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.13954.

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

pith.paper-citation-record.v1
2507.13954 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:19:30.449944Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

31 of 31 outbound references displayed

verified exact3
verified fuzzy14
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f3cd841-08b4-42b3-985e-671718b51b3b · outbound

This paper cites A tight lower bound on the controllability of networks with multiple leaders,.

Robust Anomaly Detection with Graph Neural Networks using Controllability A tight lower bound on the controllability of networks with multiple leaders,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:34.777378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb55bd85-c8f3-4f61-bc6a-8b508680105e · outbound

This paper cites A network control theory pipeline for studying the dynamics of the structural connectome,.

Robust Anomaly Detection with Graph Neural Networks using Controllability A network control theory pipeline for studying the dynamics of the structural connectome,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:34.583270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c403f72c-9fcf-42a0-ae94-375af1a1d16c · outbound

This paper cites Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory.

Robust Anomaly Detection with Graph Neural Networks using Controllability Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:19:31.490068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 626edce9-e290-4359-bfa5-66877883a0c8 · outbound

This paper cites Hamilton, Z.

Robust Anomaly Detection with Graph Neural Networks using Controllability Hamilton, Z

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:34.367324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7670c84c-79af-4022-bc97-85e25bfa6c94 · outbound

This paper cites Boost then Convolve: Gradient Boosting Meets Graph Neural Networks.

Robust Anomaly Detection with Graph Neural Networks using Controllability Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.372634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a4cfc84-b7d0-417c-91b0-b34f35e169cb · outbound

This paper cites Simplifying Graph Convolutional Networks.

Robust Anomaly Detection with Graph Neural Networks using Controllability Simplifying Graph Convolutional Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.416212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72d2b554-9cb2-47e3-812a-01f3f8009230 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Robust Anomaly Detection with Graph Neural Networks using Controllability How Powerful are Graph Neural Networks?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.507978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 607ee609-360d-4d4f-bc03-e4ef660a8444 · outbound

This paper cites Topology Adaptive Graph Convolutional Networks.

Robust Anomaly Detection with Graph Neural Networks using Controllability Topology Adaptive Graph Convolutional Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.556246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c578b4d4-335c-447d-8cb1-c933ff722854 · outbound

This paper cites DeeperGCN: All You Need to Train Deeper GCNs.

Robust Anomaly Detection with Graph Neural Networks using Controllability DeeperGCN: All You Need to Train Deeper GCNs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.611903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e89d3d5-aaf8-4264-ad64-79166fed9d9b · outbound

This paper cites Residual Gated Graph ConvNets.

Robust Anomaly Detection with Graph Neural Networks using Controllability Residual Gated Graph ConvNets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.707306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8f731fb-3688-471b-8bfe-0ba282ae789f · outbound

This paper cites How Attentive are Graph Attention Networks?.

Robust Anomaly Detection with Graph Neural Networks using Controllability How Attentive are Graph Attention Networks?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.796732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bd24338-8503-42d5-8b34-a198a21b736e · outbound

This paper cites Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification.

Robust Anomaly Detection with Graph Neural Networks using Controllability Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.870949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ee65f68-df48-48f9-b40c-6d35feae2297 · outbound

This paper cites GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection.

Robust Anomaly Detection with Graph Neural Networks using Controllability GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:19:31.164898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db770089-27f7-452c-92a3-c1d9660021fd · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

Robust Anomaly Detection with Graph Neural Networks using Controllability Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:29.042216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c10068a-3b2c-4c1f-b73b-0664a9ebf46c · outbound

This paper cites A comprehensive survey on graph anomaly detection with deep learning,.

Robust Anomaly Detection with Graph Neural Networks using Controllability A comprehensive survey on graph anomaly detection with deep learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:34.077999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 89ad9b7d-3355-478d-a1f1-9a2bee25080f · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Robust Anomaly Detection with Graph Neural Networks using Controllability Semi-Supervised Classification with Graph Convolutional Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:29.219442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db003bad-cc5a-4bf8-a1cb-fa5886bc1942 · outbound

This paper cites Inductive representation learning on large graphs,.

Robust Anomaly Detection with Graph Neural Networks using Controllability Inductive representation learning on large graphs,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:33.805713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe861273-2557-4f7d-9aa7-832d44d93188 · outbound

This paper cites A tight lower bound on the controllability of networks with multiple leaders,.

Robust Anomaly Detection with Graph Neural Networks using Controllability A tight lower bound on the controllability of networks with multiple leaders,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:33.632460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 510be02a-1206-4db4-9b68-9b82ab6c3842 · outbound

This paper cites an unresolved cited work.

Robust Anomaly Detection with Graph Neural Networks using Controllability Unresolved cited work

Reference 20

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:19:30.973563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50b4851f-0839-4016-a693-ac2f6932cace · outbound

This paper cites In: Proceedings of the 18th ACM international conference on knowl- edge discovery and data mining (SIGKDD), Beijing, China.

Robust Anomaly Detection with Graph Neural Networks using Controllability In: Proceedings of the 18th ACM international conference on knowl- edge discovery and data mining (SIGKDD), Beijing, China

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:33.418619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e4cc27a-f626-4586-ae32-1706d44b65fc · outbound

This paper cites In: Proceedings of conference credit scoring and credit control VII, pp 5–7.

Robust Anomaly Detection with Graph Neural Networks using Controllability In: Proceedings of conference credit scoring and credit control VII, pp 5–7

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:33.175906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8b8c5b0-fc7f-43b1-8138-b6006c40a46d · outbound

This paper cites Network Con- trollability in Transmodal Cortex Predicts Positive Psychosis Spec- trum Symptoms.

Robust Anomaly Detection with Graph Neural Networks using Controllability Network Con- trollability in Transmodal Cortex Predicts Positive Psychosis Spec- trum Symptoms

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T16:19:30.627567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa20d06d-44e2-47ee-a064-5737d0ad4883 · outbound

This paper cites an unresolved cited work.

Robust Anomaly Detection with Graph Neural Networks using Controllability Unresolved cited work

Reference 24

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:19:29.820952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff23d51-5042-4fd4-a985-e7c3c55fdf1a · outbound

This paper cites an unresolved cited work.

Robust Anomaly Detection with Graph Neural Networks using Controllability Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:19:33.016732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 35b92d92-bfb9-4afc-8346-84a335ad7cd2 · outbound

This paper cites Dahl, G.E.

Robust Anomaly Detection with Graph Neural Networks using Controllability Dahl, G.E

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:32.763444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 72ff40cc-7d35-4b31-87c0-dece9b60299e · outbound

This paper cites ”Class Label-aware Graph Anomaly Detection.” Proceedings of the 32nd ACM International Conference on Information and Knowledge Management.

Robust Anomaly Detection with Graph Neural Networks using Controllability ”Class Label-aware Graph Anomaly Detection.” Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:32.579985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6aa1e3c4-db0c-4677-bf3d-4876f932b331 · outbound

This paper cites ”Gad-nr: Graph anomaly detection via neighborhood reconstruction.” Proceedings of the 17th ACM International Conference on Web Search and Data Mining.

Robust Anomaly Detection with Graph Neural Networks using Controllability ”Gad-nr: Graph anomaly detection via neighborhood reconstruction.” Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:32.365230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1395750f-8a5f-4d01-83c3-d074aa4866d4 · outbound

This paper cites ”ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection.” Proceedings of the AAAI Conference on Artificial Intelligence.

Robust Anomaly Detection with Graph Neural Networks using Controllability ”ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection.” Proceedings of the AAAI Conference on Artificial Intelligence

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:32.235806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 979ef804-e486-484b-8b2b-4f23c73c2238 · outbound

This paper cites ”Graph-level anomaly de- tection via hierarchical memory networks.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases.

Robust Anomaly Detection with Graph Neural Networks using Controllability ”Graph-level anomaly de- tection via hierarchical memory networks.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:32.012532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f57677b3-ed07-4ca3-9ed0-2b2d66135d38 · outbound

This paper cites an unresolved cited work.

Robust Anomaly Detection with Graph Neural Networks using Controllability Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:19:31.811170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d003417e-3306-4732-b33b-1a103aeec26b · outbound

This paper cites ”PREM: A Simple Yet Effective Approach for Node- Level Graph Anomaly Detection.” 2023 IEEE International Conference on Data Mining (ICDM).

Robust Anomaly Detection with Graph Neural Networks using Controllability ”PREM: A Simple Yet Effective Approach for Node- Level Graph Anomaly Detection.” 2023 IEEE International Conference on Data Mining (ICDM)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:31.653039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.