Paper Citation Record · LEDGER

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2411.14094.

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

pith.paper-citation-record.v1
2411.14094 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:37:16.893958Z

measured 39 of 39 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 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

39 of 39 outbound references displayed

verified exact5
verified fuzzy18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fcc052d5-5ea7-405f-b9e1-d18e75857f41 · outbound

This paper cites Estimating Example Difficulty Using Variance of Gradients.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Estimating Example Difficulty Using Variance of Gradients

Reference 1

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Observation d8cc96b5-31d4-49e3-be1b-493a4dcc5582 · outbound

This paper cites Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:37:17.264723Z

Source-reported events for the cited work

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Observation 7b69d30a-d2ba-4510-95fb-fbe297884350 · outbound

This paper cites THE LOGICAL EXPRESSIVENESS OF GRAPH NEURAL NETWORKS.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification THE LOGICAL EXPRESSIVENESS OF GRAPH NEURAL NETWORKS

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.436748Z

Source-reported events for the cited work

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Observation ec42d1af-abe4-453a-aec5-46821a8d8e67 · outbound

This paper cites Multi-label image recognition with graph convolutional networks.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Multi-label image recognition with graph convolutional networks

Reference 4

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Observation 3b791ff5-7709-4830-b3e1-1f1585b9eb24 · outbound

This paper cites Towards a consistent evaluation of mirna- disease association prediction models.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Towards a consistent evaluation of mirna- disease association prediction models

Reference 5

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Observation fdef6787-ac4f-42a8-8b63-f02840e06407 · outbound

This paper cites Graph neural networks with learnable structural and positional representations.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Graph neural networks with learnable structural and positional representations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.414163Z

Source-reported events for the cited work

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Observation 41099ffc-48f7-4dfc-bd13-259a7d256cbe · outbound

This paper cites Inductive Representation Learning on Large Graphs.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Inductive Representation Learning on Large Graphs

Reference 7

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Observation 8018bf03-d56c-460c-8a85-f138b9b1dcc8 · outbound

This paper cites Prentice Hall PTR, 1994.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Prentice Hall PTR, 1994

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.405698Z

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Observation a3dd5037-a80e-4d00-91c3-85b2137f01bf · outbound

This paper cites Multi-label learning by exploiting label correlations locally.Proceedings of the AAAI Conference on Artificial Intelligence, 26(1):949–955, Sep.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Multi-label learning by exploiting label correlations locally.Proceedings of the AAAI Conference on Artificial Intelligence, 26(1):949–955, Sep

Reference 9

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Source-reported events for the cited work

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Observation 7f745ad9-3360-4b52-a1d3-02e890dff87d · outbound

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

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Semi-Supervised Classification with Graph Convolutional Networks

Reference 10

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Source-reported events for the cited work

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Observation 416082c0-71ac-4701-9fc0-a583cf2a1519 · outbound

This paper cites Neural message passing for multi-label classification.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Neural message passing for multi-label classification

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.397502Z

Source-reported events for the cited work

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Observation 10280551-e41c-4dbb-9c96-7de63cbd7f9c · outbound

This paper cites Improving graph neural networks with simple architecture design, 2021.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Improving graph neural networks with simple architecture design, 2021

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.389048Z

Source-reported events for the cited work

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Observation 7d3bb4ba-ebb7-471e-abb6-c32fbe6a8a92 · outbound

This paper cites Asymmetric transitivity preserving graph embedding.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Asymmetric transitivity preserving graph embedding

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.380605Z

Source-reported events for the cited work

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Observation 0e75c3c5-7898-4d32-9db0-db442d215ddf · outbound

This paper cites Deepwalk: Online learning of social representations.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Deepwalk: Online learning of social representations

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.372278Z

Source-reported events for the cited work

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Observation 1125e65c-46d7-4cc6-a77c-f25d5d5f0a8d · outbound

This paper cites Galaxc: Graph neural networks with labelwise attention for extreme classification.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Galaxc: Graph neural networks with labelwise attention for extreme classification

Reference 15

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Source-reported events for the cited work

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Observation e2f2bc95-f1a7-4434-9711-07be94ee7685 · outbound

This paper cites Training-free graph neural networks and the power of labels as features, 2024.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Training-free graph neural networks and the power of labels as features, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.363731Z

Source-reported events for the cited work

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Observation 6505dcb0-9834-42c4-8fac-10050524cafb · outbound

This paper cites Multi-Label Graph Convolutional Network Representation Learning.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Multi-Label Graph Convolutional Network Representation Learning

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:37:17.173926Z

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 ee2a1d67-5ea1-4a7e-913d-be7b76cc0292 · outbound

This paper cites Metadata archaeology: Unearthing data subsets by leveraging training dynamics, 2022.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Metadata archaeology: Unearthing data subsets by leveraging training dynamics, 2022

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.354970Z

Source-reported events for the cited work

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Observation d5db450a-1561-4899-b409-e09f33847c18 · outbound

This paper cites Semi-supervised multi- label learning for graph-structured data.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Semi-supervised multi- label learning for graph-structured data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.346513Z

Source-reported events for the cited work

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Observation ea28b0db-d8e5-49c8-adbe-6134fceac009 · outbound

This paper cites On the Equivalence between Positional Node Embeddings and Structural Graph Representations.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification On the Equivalence between Positional Node Embeddings and Structural Graph Representations

Reference 21

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Observation f30b9e91-e1a0-430b-ab74-8bb0d12e698e · outbound

This paper cites Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T15:37:16.836277Z

Source-reported events for the cited work

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Observation 55c6b6c5-38b6-4745-baec-f31c29005827 · outbound

This paper cites Relational learning via latent social dimensions.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Relational learning via latent social dimensions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.337244Z

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Observation 9142831a-964d-4f35-8f2f-479d6042c595 · outbound

This paper cites Graph Attention Networks.International Conference on Learning Representations, 2018.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Graph Attention Networks.International Conference on Learning Representations, 2018

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.328092Z

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Observation dc6ee088-af3c-41ca-bcc4-e19c43e7eefa · outbound

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GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Unresolved cited work

Reference 25

Resolution: verified exact; doi, observed 2026-08-12T15:37:16.918180Z

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Observation e08d8fe8-e55a-4992-9f1b-d2781690d7ff · outbound

This paper cites Unifying Graph Convolutional Neural Networks and Label Propagation.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Unifying Graph Convolutional Neural Networks and Label Propagation

Reference 26

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Observation e2dbb935-deb9-4422-8d55-0dface57864d · outbound

This paper cites How Powerful are Graph Neural Networks?.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification How Powerful are Graph Neural Networks?

Reference 27

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Observation 6a49c9bc-150d-40b7-8026-12860f12c34c · outbound

This paper cites Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.318732Z

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Observation 89693ca3-042e-4595-be82-357bc215e4f6 · outbound

This paper cites Breaking the expression bottleneck of graph neural networks.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Breaking the expression bottleneck of graph neural networks

Reference 29

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Observation e05ff66e-062f-4126-9283-279235cf150a · outbound

This paper cites Evaluating link prediction methods.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Evaluating link prediction methods

Reference 30

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Observation 4e843c95-cbc5-43b9-9ec5-2b5b7e7a38c3 · outbound

This paper cites Identity-aware Graph Neural Networks.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Identity-aware Graph Neural Networks

Reference 31

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Observation ba6e9bbe-6d3e-408c-b115-5cad7b228c50 · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T15:37:16.871485Z

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Observation 23637411-6873-4c11-9282-3272d892d2f6 · outbound

This paper cites Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T15:37:16.874603Z

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Observation 9bead6a2-5056-45a1-a625-0d54d2d4af0b · outbound

This paper cites Multi-label node classification on graph-structured data.Transactions on Machine Learn- ing Research, 2023.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Multi-label node classification on graph-structured data.Transactions on Machine Learn- ing Research, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.300659Z

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Observation 69a1abf8-7a24-4aa6-bac1-d6ed59bf12d0 · outbound

This paper cites Towards Data-centric Graph Machine Learning: Review and Outlook.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Towards Data-centric Graph Machine Learning: Review and Outlook

Reference 35

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Observation 47ec93ae-f631-4de6-a371-c282cba3c391 · outbound

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GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Unresolved cited work

Reference 36

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Observation f33c48c9-0d0b-4a0a-aee0-0a55b83c4c5e · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.291574Z

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Observation 1ec1b940-1ac5-4d03-b629-86a93ccb863e · outbound

This paper cites Multi-Label Learning with Global and Local Label Correlation.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification Multi-Label Learning with Global and Local Label Correlation

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:37:16.940166Z

Source-reported events for the cited work

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Observation 99ab1d59-b8dd-46f9-ad7c-104687c7148a · outbound

This paper cites OOM" denotes the.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification OOM" denotes the

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:37:17.282290Z

Source-reported events for the cited work

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Observation 4a04a882-35ae-4ee4-9f90-5e9261f71fc3 · outbound

This paper cites ISBN 9781605584959.

GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification ISBN 9781605584959

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-12T15:37:16.843310Z

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Pith citing papers

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