Paper Citation Record · LEDGER

Graph as a feature: improving node classification with non-neural graph-aware logistic regression

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2411.12330.

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

pith.paper-citation-record.v1
2411.12330 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:44:04.365585Z

measured 43 of 43 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.

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

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f6ed45e-92ec-4cc2-bbda-1b8a62f802dc · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Pitfalls of Graph Neural Network Evaluation

Reference 1

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Observation 08c1a047-3c04-4896-951f-de1b55833cc4 · outbound

This paper cites Simple and deep graph convolutional networks.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Simple and deep graph convolutional networks

Reference 2

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Observation a153586a-8389-4fc4-8e7c-f8ed8e1b8842 · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily

Reference 3

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This paper cites Are we really making much progress? A worrying analysis of recent neural recommendation approaches.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Are we really making much progress? A worrying analysis of recent neural recommendation approaches

Reference 4

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Observation 9d59f4f0-440d-4722-a7ef-30900aa1acf2 · outbound

This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Predict then propagate: Graph neural networks meet personalized pagerank

Reference 5

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Observation 182b1447-9f51-4b7b-bdda-062181481985 · outbound

This paper cites Homophily-oriented heterogeneous graph rewiring.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Homophily-oriented heterogeneous graph rewiring

Reference 6

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Observation f1131dbf-07a4-4a97-b3fb-fc7c40b73b0f · outbound

This paper cites Inductive representation learning on large graphs.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Inductive representation learning on large graphs

Reference 7

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Observation e5340c95-51a5-4a73-ab7a-d3ff854a95c5 · outbound

This paper cites Lightgcn: Simplifying and powering graph convolution network for recommendation.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 8

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Observation a8a6fd0f-9dc3-471c-acdd-3b7dad3a599b · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Open graph benchmark: Datasets for machine learning on graphs

Reference 9

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Observation d531ef59-9f29-4180-bcb1-78799c8d397c · outbound

This paper cites Combining Label Propagation and Simple Models Out-performs Graph Neural Networks.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

Reference 10

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Observation ede39c45-d254-4320-a0a5-d7bebadfcac9 · outbound

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

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Semi-supervised classification with graph convolutional networks

Reference 11

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Observation 6c756ae4-b078-4b71-b564-82fa00a2e12f · outbound

This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Deeper insights into graph convolutional networks for semi-supervised learning

Reference 12

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Observation 84e8496c-db64-4c94-8160-a7846ad17f85 · outbound

This paper cites Beyond low-pass filters: Adaptive feature propagation on graphs.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Beyond low-pass filters: Adaptive feature propagation on graphs

Reference 13

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Observation c19c3906-11ea-491c-969b-86cd560ffec1 · outbound

This paper cites Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:44:05.162626Z

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Observation 96fa3e51-86d3-4fa4-846d-fd40ff3c3c42 · outbound

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

Graph as a feature: improving node classification with non-neural graph-aware logistic regression New Benchmarks for Learning on Non-Homophilous Graphs

Reference 15

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Observation 2224a48d-5b48-4ca9-818c-a7cc659433e2 · outbound

This paper cites Semi-supervised classification of network data using very few labels.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Semi-supervised classification of network data using very few labels

Reference 16

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Observation db52db31-e869-484f-a288-8752a1a14a26 · outbound

This paper cites Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research

Reference 17

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Observation 54888d18-9477-43bd-a231-3cacacafb77a · outbound

This paper cites Taxonomy of benchmarks in graph representation learning.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Taxonomy of benchmarks in graph representation learning

Reference 18

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Observation 6a8a1720-33c4-46d9-b552-92cbcda7c09c · outbound

This paper cites Graph neural networks with adaptive residual.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Graph neural networks with adaptive residual

Reference 19

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Observation d1a2752e-7a54-4115-a2bd-446ee8b35e82 · outbound

This paper cites Meta-weight graph neural network: Push the limits beyond global homophily.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Meta-weight graph neural network: Push the limits beyond global homophily

Reference 20

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Observation 10a5ed22-d445-4e56-9696-58241be6efdb · outbound

This paper cites A simple relational classifier.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression A simple relational classifier

Reference 21

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Observation 5ec6c2c5-72ab-4431-91f4-91fa0a58a417 · outbound

This paper cites Beyond real-world benchmark datasets: An empirical study of node classification with gnns.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Beyond real-world benchmark datasets: An empirical study of node classification with gnns

Reference 22

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Observation 79684b59-e0d9-4b1b-b9f6-df28e2c5bad0 · outbound

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Ultragcn: ultra simplification of graph convolutional networks for recommendation

Reference 23

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This paper cites Image-based recommendations on styles and substitutes.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Image-based recommendations on styles and substitutes

Reference 24

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Iterative classification in relational data

Reference 25

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Observation cb249ff7-1bcf-4d55-b0cf-08ce3d1e1623 · outbound

This paper cites GraphWorld : Fake Graphs Bring Real Insights for GNNs.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression GraphWorld : Fake Graphs Bring Real Insights for GNNs

Reference 26

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

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Geom-gcn: Geometric graph convolutional networks

Reference 27

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Observation acf8b14c-847a-42a0-b399-6cae15a344df · outbound

This paper cites A critical look at the evaluation of gnns under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression A critical look at the evaluation of gnns under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023

Reference 28

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Observation f738e325-093f-497b-9c2f-4bc694845ff6 · outbound

This paper cites Near linear time algorithm to detect community structures in large-scale networks.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Near linear time algorithm to detect community structures in large-scale networks

Reference 29

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Graph attention networks

Reference 30

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

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Label information enhanced fraud detection against low homophily in graphs

Reference 31

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This paper cites Simplifying graph convolutional networks.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Simplifying graph convolutional networks

Reference 32

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Observation c3b514c2-07e9-49fe-a354-3439d7c09bc3 · outbound

This paper cites Learning to augment graph structure for both homophily and heterophily graphs.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Learning to augment graph structure for both homophily and heterophily graphs

Reference 33

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This paper cites A comprehensive survey on graph neural networks.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression A comprehensive survey on graph neural networks

Reference 34

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Representation learning on graphs with jumping knowledge networks

Reference 35

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Revisiting semi-supervised learning with graph embeddings

Reference 36

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This paper cites Design space for graph neural networks.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Design space for graph neural networks

Reference 37

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Observation 4f501487-1828-454b-9504-b261e227dff4 · outbound

This paper cites To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles

Reference 38

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This paper cites Simplifying node classification on heterophilous graphs with compatible label propagation.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Simplifying node classification on heterophilous graphs with compatible label propagation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:44:05.033113Z

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Graph neural networks: A review of methods and applications

Reference 40

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

Graph as a feature: improving node classification with non-neural graph-aware logistic regression Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:44:05.024408Z

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Graph as a feature: improving node classification with non-neural graph-aware logistic regression Semi-supervised learning with graphs

Reference 42

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Observation 9cdd9e88-7045-487b-adb7-5732146baeea · outbound

This paper cites write newline.

Graph as a feature: improving node classification with non-neural graph-aware logistic regression write newline

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Pith citing papers

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