Paper Citation Record · LEDGER

Partitioning Message Passing for Graph Fraud Detection

As of 13 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 6 inbound Pith citation observations for arXiv:2412.00020.

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

pith.paper-citation-record.v1
2412.00020 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:23:08.141351Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:36:02.220999Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T06:54:21.426067Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy13
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e661816-0c57-40f8-9007-c0ee370da651 · outbound

This paper cites A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions.

Partitioning Message Passing for Graph Fraud Detection A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T19:23:08.079447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b662b84-970d-4ed0-bd0b-99cb8eb42a08 · outbound

This paper cites Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?.

Partitioning Message Passing for Graph Fraud Detection Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T19:23:08.083437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536c6ce2-c9e2-45bb-8fdd-c80a5029e80e · outbound

This paper cites Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi.

Partitioning Message Passing for Graph Fraud Detection Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.416809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 531a1e8a-69e2-4d56-9606-3c830e4f5b92 · outbound

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

Partitioning Message Passing for Graph Fraud Detection Label information enhanced fraud detection against low homophily in graphs

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.405022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 031e2b8c-5b23-43fd-b43c-4ad832170aa5 · outbound

This paper cites Interpreting and unifying graph neural networks with an optimization framework.

Partitioning Message Passing for Graph Fraud Detection Interpreting and unifying graph neural networks with an optimization framework

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.382167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 28f221b7-09e0-4c4e-b62a-77b996f6060b · outbound

This paper cites Diffusiongan: Network embedding for informa- tion diffusion prediction with generative adversarial nets.

Partitioning Message Passing for Graph Fraud Detection Diffusiongan: Network embedding for informa- tion diffusion prediction with generative adversarial nets

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.371917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7d1a1d53-25e8-48de-a191-c08d94707744 · outbound

This paper cites For the sake of simplicity, we analyze the first layer of PMP as an example and omit superscripts.

Partitioning Message Passing for Graph Fraud Detection For the sake of simplicity, we analyze the first layer of PMP as an example and omit superscripts

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.349302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2155b7a7-fa4f-4544-9f56-db6a21340406 · outbound

This paper cites C, representing the number of classes, is set to 2 for GFD.

Partitioning Message Passing for Graph Fraud Detection C, representing the number of classes, is set to 2 for GFD

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.337959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 65fb865f-823f-4b40-8a62-748cc6d0d5b3 · outbound

This paper cites Due to the anonymity and privacy policy, we exclude the exact details of the graph but roughly the graph includes over 1 million nodes and over 10 million edges.

Partitioning Message Passing for Graph Fraud Detection Due to the anonymity and privacy policy, we exclude the exact details of the graph but roughly the graph includes over 1 million nodes and over 10 million edges

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.327382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 648dfb4a-dd30-4aca-8890-4337547cdf04 · outbound

This paper cites an unresolved cited work.

Partitioning Message Passing for Graph Fraud Detection Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:23:08.315718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a67a8fc6-3489-4fc0-a683-09f00929fb01 · outbound

This paper cites an unresolved cited work.

Partitioning Message Passing for Graph Fraud Detection Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:23:08.303699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3de3343a-bb56-4f45-95f7-ef5ee7e743c8 · outbound

This paper cites Geom-gcn: Geometric graph convolutional networks.

Partitioning Message Passing for Graph Fraud Detection Geom-gcn: Geometric graph convolutional networks

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.447488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0142675c-0b52-44b4-a0d2-653e9a0233c3 · outbound

This paper cites Cadue: Content- agnostic detection of unwanted emails for enterprise security.

Partitioning Message Passing for Graph Fraud Detection Cadue: Content- agnostic detection of unwanted emails for enterprise security

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.457789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 95a5c08e-94e7-4fa7-b6e7-70c2081dffcb · outbound

This paper cites Modeling relational data with graph convolutional networks.

Partitioning Message Passing for Graph Fraud Detection Modeling relational data with graph convolutional networks

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.437430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 99546aef-9171-4540-91ec-66f5d5aaf70f · outbound

This paper cites Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.

Partitioning Message Passing for Graph Fraud Detection Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T19:23:08.067118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfb1a02d-4f3b-4d45-9b9f-796d143d0f73 · outbound

This paper cites New Benchmarks for Learning on Non-Homophilous Graphs.

Partitioning Message Passing for Graph Fraud Detection New Benchmarks for Learning on Non-Homophilous Graphs

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T19:23:08.075107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5bba752-1f12-4499-b653-09fa9e5c9425 · outbound

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

Partitioning Message Passing for Graph Fraud Detection H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.427102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 312f13b7-6a42-4a5c-b4c3-f351b2eac37d · outbound

This paper cites an unresolved cited work.

Partitioning Message Passing for Graph Fraud Detection Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:23:08.360742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1db6ff56-5b10-4bce-b46b-693d8df4d2f0 · outbound

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

Partitioning Message Passing for Graph Fraud Detection Ad- dressing heterophily in graph anomaly detection: A perspective of graph spectrum

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.467438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df7bcc5a-67d6-40c6-b2bb-c015ef0a3d57 · outbound

This paper cites Masked Contrastive Learning for Anomaly Detection.

Partitioning Message Passing for Graph Fraud Detection Masked Contrastive Learning for Anomaly Detection

Reference 2021

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T19:23:08.292247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 65dbd33e-512e-47c5-826d-08ff94f30d63 · outbound

This paper cites Shebuti Rayana and Leman Akoglu.

Partitioning Message Passing for Graph Fraud Detection Shebuti Rayana and Leman Akoglu

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T19:23:08.095485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a1946c6-fdeb-497c-b363-e39f7f0929a9 · outbound

This paper cites A comprehensive survey on graph neural networks.

Partitioning Message Passing for Graph Fraud Detection A comprehensive survey on graph neural networks

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:23:08.394173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 25c85ca9-7ef8-4859-98d0-1a380820499b · inbound

DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs cites this paper.

DGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs Partitioning Message Passing for Graph Fraud Detection

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T12:36:02.220999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3439a33-d9f0-43d0-853c-38aa7b50dc8e · inbound

UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains cites this paper.

UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains Partitioning Message Passing for Graph Fraud Detection

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:26:03.385751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eb1d09ae-fcd4-49af-8867-faeebd29cf0f · inbound

Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection cites this paper.

Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection Partitioning Message Passing for Graph Fraud Detection

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:32:24.546727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a0f2c3c7-323f-4379-b7ad-bcf5a25f879a · inbound

L2IR: Revealing Latent Intent in Graph Fraud Detection cites this paper.

L2IR: Revealing Latent Intent in Graph Fraud Detection Partitioning Message Passing for Graph Fraud Detection

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:43:58.689014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c908d363-b096-432c-a3c0-1f33d5a78d75 · inbound

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection cites this paper.

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection Partitioning Message Passing for Graph Fraud Detection

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-29T06:43:10.321595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 94ba9cb2-092f-4ffd-b1eb-7cd21dfe1a38 · inbound

Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection cites this paper.

Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection Partitioning Message Passing for Graph Fraud Detection

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:54:21.427632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.