Paper Citation Record · LEDGER

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.04608.

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

pith.paper-citation-record.v1
2506.04608 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:44:46.026851Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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verified fuzzy23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 879cbd52-2662-45d4-a01c-23c8ec8c86ec · outbound

This paper cites Anomaly pattern detection in high-frequency trading using graph neural networks.Journal of Industrial Engineering and Applied Science, 2(6):77–85, 2024.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Anomaly pattern detection in high-frequency trading using graph neural networks.Journal of Industrial Engineering and Applied Science, 2(6):77–85, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:49.032004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 587ddc6b-fefb-4309-a35d-3b97ad9d6b07 · outbound

This paper cites Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:44:45.905581Z

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Observation 76e32aa5-ac1c-47d5-95f6-0f2bcd92a5c3 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations How Powerful are Graph Neural Networks?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:44:45.909398Z

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Observation ede3a13d-4b49-4375-ba36-668b1fb8110b · outbound

This paper cites Understanding artificial intelligence ethics and safety.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Understanding artificial intelligence ethics and safety

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:44:45.912898Z

Source-reported events for the cited work

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Observation 7054c848-0820-41df-89ec-4d10634c0a79 · outbound

This paper cites Ai in the uk: ready, willing and able?Retrieved August, 13:supra note 20, 95–100., 2018.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Ai in the uk: ready, willing and able?Retrieved August, 13:supra note 20, 95–100., 2018

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:48.859131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3e825114-30f0-4eec-b75f-3dc5d8b030e5 · outbound

This paper cites Pat: Towards flexible verification under fairness.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Pat: Towards flexible verification under fairness

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:48.701781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 03e4370d-0af5-43a4-b5fd-fbb3d36030df · outbound

This paper cites Formal methods: State of the art and future directions.ACM Computing Surveys (CSUR), 28(4):626–643, 1996.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Formal methods: State of the art and future directions.ACM Computing Surveys (CSUR), 28(4):626–643, 1996

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:48.472392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d0231d01-d54d-42fd-9d25-b1543b4dafdb · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Gnnexplainer: Generating explanations for graph neural networks.Advances in neural information processing systems, 32, 2019

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:44:45.926109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc05752-a598-4399-a9d0-871a37c8b20c · outbound

This paper cites Parameterized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Parameterized explainer for graph neural network.Advances in neural information processing systems, 33:19620–19631, 2020

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:44:45.929133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 588a638c-7cfd-486c-82de-bbb7ad32d406 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:48.252478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eeddb206-fffe-4a61-977d-e98043403f7b · outbound

This paper cites Edge-labeling graph neural network for few-shot learning.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Edge-labeling graph neural network for few-shot learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:48.066611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 80017e26-a0c1-49ad-95a1-fca8eee3eccf · outbound

This paper cites Con- volutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Con- volutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:47.827623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c81fcbbf-244a-46c8-b6f5-e73ba5fa5ee0 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Explainability in graph neural networks: A taxonomic survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:47.656001Z

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Observation d64b77d9-c9e1-4082-bde2-232bf8ed3319 · outbound

This paper cites Digraph inception convolutional networks.Advances in neural information processing systems, 33:17907–17918, 2020.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Digraph inception convolutional networks.Advances in neural information processing systems, 33:17907–17918, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:47.483217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a46524cb-a9a8-4dc2-a909-cc6972f8b785 · outbound

This paper cites GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks

Reference 15

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Source-reported events for the cited work

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Observation 4eb63ee7-dd42-494d-b5f3-0ee1a8c659bd · outbound

This paper cites Approximate von neumann entropy for directed graphs.Physical Review E, 89(5):052804, 2014.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Approximate von neumann entropy for directed graphs.Physical Review E, 89(5):052804, 2014

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:47.339327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72d9c21c-229b-4393-ae69-2bbf02580a63 · outbound

This paper cites Statistical mechanics of complex networks.Reviews of modern physics, 74(1):47, 2002.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Statistical mechanics of complex networks.Reviews of modern physics, 74(1):47, 2002

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:47.139007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5885de7c-5fb6-443e-a038-8866e1af135c · outbound

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

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Semi-Supervised Classification with Graph Convolutional Networks

Reference 18

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Source-reported events for the cited work

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Observation 64961f74-89ec-49ab-825c-404b55d89b9a · outbound

This paper cites Directed graph contrastive learning.Advances in neural information processing systems, 34:19580–19593, 2021.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Directed graph contrastive learning.Advances in neural information processing systems, 34:19580–19593, 2021

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.925484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7d9c247b-c091-4011-8359-4f0eb5f2af19 · outbound

This paper cites Neural graph collaborative filtering.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Neural graph collaborative filtering

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.699050Z

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Observation 60685e53-2dae-43d4-94a8-e0817cd698ca · outbound

This paper cites an unresolved cited work.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:44:46.527795Z

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Observation a92fc4f1-4398-45e0-99eb-93e1d6a92e33 · outbound

This paper cites On differentially private graph sparsification and applications.Advances in neural information processing systems, 32, 2019.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations On differentially private graph sparsification and applications.Advances in neural information processing systems, 32, 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.495511Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1da7600-55c7-4919-9709-a62e4ac5abd7 · outbound

This paper cites Spectral Networks and Locally Connected Networks on Graphs.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Spectral Networks and Locally Connected Networks on Graphs

Reference 23

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Observation 73c72b73-1476-473f-9008-11f2cfdaf4ed · outbound

This paper cites Magnet: A neural network for directed graphs.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Magnet: A neural network for directed graphs

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.469250Z

Source-reported events for the cited work

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Observation ade519d5-ab30-46b5-bd53-e21d3192d2a8 · outbound

This paper cites Edge directionality improves learning on heterophilic graphs.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Edge directionality improves learning on heterophilic graphs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.442101Z

Source-reported events for the cited work

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Observation c63176bd-a607-48fe-b752-2cf11a7fb866 · outbound

This paper cites Inductive repre- sentation learning on large graphs.Advances in neural information processing systems, 30, 2017.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Inductive repre- sentation learning on large graphs.Advances in neural information processing systems, 30, 2017

Reference 26

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Observation 3fdea2a4-13c6-4028-bd46-ed0f8bf81ba7 · outbound

This paper cites Graph Attention Networks.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Graph Attention Networks

Reference 27

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Observation 9b86a557-fc39-4d92-b103-5878d1ed1530 · outbound

This paper cites A new model for learning in graph domains.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations A new model for learning in graph domains

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:44:45.986540Z

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Observation 7137c28e-c9c8-42e6-88ac-fc0fc4c2d5b9 · outbound

This paper cites The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations The graph neural network model.IEEE transactions on neural networks, 20(1):61–80, 2008

Reference 29

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Observation a2addf32-b254-4405-916d-83b7edc52abd · outbound

This paper cites Spectral-based Graph Convolutional Network for Directed Graphs.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Spectral-based Graph Convolutional Network for Directed Graphs

Reference 30

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Observation 08977b41-f896-4297-9a78-2205a13771d5 · outbound

This paper cites Peeking inside the black- box: a survey on explainable artificial intelligence (xai).IEEE access, 6:52138–52160, 2018.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Peeking inside the black- box: a survey on explainable artificial intelligence (xai).IEEE access, 6:52138–52160, 2018

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.397279Z

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Observation dc7f79d4-ea61-4884-a31e-aac8d4acb18b · outbound

This paper cites an unresolved cited work.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Unresolved cited work

Reference 32

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Observation b6e40947-191f-42bd-81bd-f25c698ec723 · outbound

This paper cites A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018

Reference 33

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Observation 440a0067-d56b-4678-b82b-84537b8d7d73 · outbound

This paper cites Understanding black-box predictions via influence functions.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Understanding black-box predictions via influence functions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.365185Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a929a314-daeb-4f52-b4a3-20865cc8f5c0 · outbound

This paper cites Modern graph neural networks primarily follow spectral or spatial paradigms with varying directional awareness.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Modern graph neural networks primarily follow spectral or spatial paradigms with varying directional awareness

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.355575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c154a87a-1c8a-482f-9a8c-fd60e08994d9 · outbound

This paper cites While GIN [3] theoretically handles directionality through injective aggre- gation, its isomorphism focus favors undirected implemen- tations.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations While GIN [3] theoretically handles directionality through injective aggre- gation, its isomorphism focus favors undirected implemen- tations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.345449Z

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Observation 06cc7f2e-02eb-4a20-b143-74c1e13fc0fd · outbound

This paper cites As shown in the visualization, the symmetric Figure 4.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations As shown in the visualization, the symmetric Figure 4

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.335882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7cc07c3a-d428-4fc5-8b8b-496a14b02b8b · outbound

This paper cites Additionally, it extends these findings across multiple GNN architectures, highlighting the broad applica- bility of our approach.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Additionally, it extends these findings across multiple GNN architectures, highlighting the broad applica- bility of our approach

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.326467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dfc5b920-cdc8-4542-b00c-99f0731da2ad · outbound

This paper cites Notable works include Graph Convolution Network (GCN) [18], GraphSAGE [26], and Graph Attention Net- work (GAT) [27].

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Notable works include Graph Convolution Network (GCN) [18], GraphSAGE [26], and Graph Attention Net- work (GAT) [27]

Reference 39

Resolution: verified exact; raw_fallback, observed 2026-08-07T10:44:46.221721Z

Source-reported events for the cited work

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Observation 9686a0f0-1e92-41b2-b1f4-eb47aeb51615 · outbound

This paper cites Post-hoc explanation methods [31]–[34] have been widely adopted, viewing models as black boxes while probing for relevant information.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Post-hoc explanation methods [31]–[34] have been widely adopted, viewing models as black boxes while probing for relevant information

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:46.317943Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ffd1843e-fe82-44d2-b797-edde617630d5 · outbound

This paper cites an unresolved cited work.

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:44:46.309106Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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