Paper Citation Record · LEDGER

Addressing Noise and Stochasticity in Fraud Detection for Service Networks

As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2505.00946.

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

pith.paper-citation-record.v1
2505.00946 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:37:09.599677Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:56:02.920597Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:11:15.440602Z

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy30
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1b90e23-1632-4738-9f1c-fa0cf22b8dc1 · outbound

This paper cites Improving hotels’ operational efficiency through esg investment: A risk management perspective,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Improving hotels’ operational efficiency through esg investment: A risk management perspective,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.875901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a46734a9-90c7-4680-b956-8ad6c968471f · outbound

This paper cites Recommending products and services belonging to online businesses using intelligent agents,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Recommending products and services belonging to online businesses using intelligent agents,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.867846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 97684c9e-deb6-4862-8426-e32bdac62df1 · outbound

This paper cites Sefraud: Graph-based self- explainable fraud detection via interpretative mask learning,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Sefraud: Graph-based self- explainable fraud detection via interpretative mask learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.859450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 50c9d913-8737-4f10-9c22-1a93f3930f6b · outbound

This paper cites Intention-aware heterogeneous graph attention networks for fraud transactions detection,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Intention-aware heterogeneous graph attention networks for fraud transactions detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.850926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9733bd4e-179f-4d68-9c95-bdb130ddb0e2 · outbound

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

Addressing Noise and Stochasticity in Fraud Detection for Service Networks H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.842336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93a52c4f-0e60-4cf5-a2e6-47ff79dc9a07 · outbound

This paper cites A gnn-based fraud detector with dual resistance to graph disassortativity and imbalance,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks A gnn-based fraud detector with dual resistance to graph disassortativity and imbalance,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.834019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ac267696-0b9b-4738-8bb0-ae0259db5617 · outbound

This paper cites Splitgnn: Spectral graph neural network for fraud detection against heterophily,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Splitgnn: Spectral graph neural network for fraud detection against heterophily,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.825941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c91f69b-9552-4c0d-82ba-88ac11b79981 · outbound

This paper cites Cross-modal clustering with deep correlated information bottleneck method,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Cross-modal clustering with deep correlated information bottleneck method,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.817576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc747d5a-dc88-4ed4-8ba9-6a4dbd6fc7c0 · outbound

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

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Enhancing graph neural network-based fraud detectors against camouflaged fraud- sters,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.809513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6e9b2f4a-8f8b-4863-90e4-307ae144a8fd · outbound

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

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Pick and choose: A gnn-based imbalanced learning approach for fraud detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.800848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3d05dd5f-4ca5-4806-8788-2c1975b80019 · outbound

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

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Dig- in-gnn: Discriminative feature guided gnn-based fraud detector against inconsistencies in multi-relation fraud graph,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.792485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 54dc1f27-9015-48c4-9441-c41be7bb754b · outbound

This paper cites Asa-gnn: Adaptive sampling and aggregation-based graph neural network for transaction fraud detection,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Asa-gnn: Adaptive sampling and aggregation-based graph neural network for transaction fraud detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.784621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2d4acc3b-d446-4134-8fbf-934f4dea4e63 · outbound

This paper cites Alleviating the incon- sistency problem of applying graph neural network to fraud detection,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Alleviating the incon- sistency problem of applying graph neural network to fraud detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.776450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2dd34de1-5938-44e6-937f-aedfc4b10917 · outbound

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

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Label information enhanced fraud detection against low homophily in graphs,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.768380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 031279c3-b516-4b8b-b410-8fc670dae2fc · outbound

This paper cites Can abnormality be detected by graph neural networks?.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Can abnormality be detected by graph neural networks?

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.759944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00c5362c-deab-4503-a444-a91c3fb006b3 · outbound

This paper cites Rethinking graph neural networks for anomaly detection,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Rethinking graph neural networks for anomaly detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.751981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 65ed3f44-ff21-41a9-a644-d98b964e401a · outbound

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

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Semi-supervised classification with graph convolutional networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.743168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ca001119-3d87-483b-9781-7127a9fb97fe · outbound

This paper cites Adaptive filters in graph convolutional neural networks,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Adaptive filters in graph convolutional neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.735149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f89a44f5-411f-4fc8-8b31-789dd25169dc · outbound

This paper cites Graph neural networks with learnable and optimal polynomial bases,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Graph neural networks with learnable and optimal polynomial bases,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.725806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9e5ba18-4eb8-4920-9d31-be3bd894f3b1 · outbound

This paper cites Polyformer: Scalable node-wise filters via polynomial graph transformer,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Polyformer: Scalable node-wise filters via polynomial graph transformer,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.717457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b56aab5-8a51-4ed7-80e5-3be3f81d4e28 · outbound

This paper cites Node-oriented spectral filtering for graph neural networks,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Node-oriented spectral filtering for graph neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.709153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 687ae27d-1f79-44d8-9e9f-c757e3d687bc · outbound

This paper cites Graph attention networks,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Graph attention networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.700616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ba2e8e3-2532-48d8-8f97-be08cb1167b3 · outbound

This paper cites Inductive representation learning on large graphs,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Inductive representation learning on large graphs,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.692628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3943cbd6-6222-4a9a-98da-1d110cfacc45 · outbound

This paper cites Edgeless-gnn: Unsupervised representation learning for edgeless nodes,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Edgeless-gnn: Unsupervised representation learning for edgeless nodes,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.683939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fe64074a-78b9-44f0-bb5a-894fd262ca12 · outbound

This paper cites Trajectory-user linking via multi-scale graph attention network,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Trajectory-user linking via multi-scale graph attention network,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.675367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a0f8005-2fe3-4894-b590-5d8ead9cb189 · outbound

This paper cites Heterogeneous graph attention networks for depression identification by campus cyber- activity patterns,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Heterogeneous graph attention networks for depression identification by campus cyber- activity patterns,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.666764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 687fe9a9-fdef-4287-848e-fba1dab9c592 · outbound

This paper cites Two-level graph neural network,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Two-level graph neural network,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.657895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cad449e9-88f5-4f86-9361-7f758afc05a3 · outbound

This paper cites Specformer: Spectral graph neural networks meet transformers,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Specformer: Spectral graph neural networks meet transformers,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.649562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 75f3eaaa-ecd6-4b3c-96f6-32c85b7c6e05 · outbound

This paper cites Xgboost: A scalable tree boosting system,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Xgboost: A scalable tree boosting system,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T04:37:09.593401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f6ac227-4440-4c8f-991d-3e0386faef3a · outbound

This paper cites Beyond low-frequency informa- tion in graph convolutional networks,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Beyond low-frequency informa- tion in graph convolutional networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.635998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f11a1ea0-4070-4889-a019-03771094a031 · outbound

This paper cites Adaptive universal generalized pagerank graph neural network,.

Addressing Noise and Stochasticity in Fraud Detection for Service Networks Adaptive universal generalized pagerank graph neural network,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:37:09.626744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation eafc6215-46f5-4fe5-b0b4-6c63e0b63291 · inbound

Multilingual Source Tracing of Speech Deepfakes: A First Benchmark cites this paper.

Multilingual Source Tracing of Speech Deepfakes: A First Benchmark Addressing Noise and Stochasticity in Fraud Detection for Service Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T00:56:02.920597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97e2fe7e-a93c-48dc-b7ad-011e3939c073 · inbound

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative cites this paper.

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative Addressing Noise and Stochasticity in Fraud Detection for Service Networks

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:11:15.484079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.