Paper Citation Record · LEDGER

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications

As of 11 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2501.12309.

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

pith.paper-citation-record.v1
2501.12309 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:23:20.181104Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 55fb1d5a-7212-44ae-abed-873e2765963b · outbound

This paper cites Social Media Based Recommender System for E- Com- merce Platforms,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Social Media Based Recommender System for E- Com- merce Platforms,

Reference 1

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Observation dde8b440-7b42-4392-aa5a-d12c820a6083 · outbound

This paper cites Deep Learning for Recommender Systems: A Netflix Case Study,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Deep Learning for Recommender Systems: A Netflix Case Study,

Reference 2

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This paper cites Deep Neural Networks for YouTube Recommendations,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Deep Neural Networks for YouTube Recommendations,

Reference 3

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This paper cites A comprehensive survey on graph neural networks,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications A comprehensive survey on graph neural networks,

Reference 4

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This paper cites DeepWalk: Online Learning of Social Representations.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications DeepWalk: Online Learning of Social Representations

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This paper cites Line: Large-scale information network embedding,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Line: Large-scale information network embedding,

Reference 6

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Observation b0f6b19a-cc3f-4459-8c66-555d86943aff · outbound

This paper cites node2vec: Scalable Feature Learning for Networks,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications node2vec: Scalable Feature Learning for Networks,

Reference 7

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Observation 6468157e-6d71-4988-9538-dc0d8502db60 · outbound

This paper cites Representation learning on graphs: Methods and applications,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Representation learning on graphs: Methods and applications,

Reference 8

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Observation c8a1ad23-a1e8-45af-8ec5-9ecffa853bc9 · outbound

This paper cites Graph neural networks: A review of methods and applications,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Graph neural networks: A review of methods and applications,

Reference 9

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Observation 46d8b645-8586-4a3c-8a3d-74bb7b5552ad · outbound

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

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Spectral Networks and Locally Connected Networks on Graphs

Reference 10

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Observation a608023a-2c13-4aff-9cde-86214c62c737 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Convolutional neural networks on graphs with fast localized spectral filtering,

Reference 11

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Observation b2dfbf90-995d-494a-b658-7750b2497d63 · outbound

This paper cites The emerging field of signal processing on graphs: Ex- tending high-dimensional data analysis to networks and other irregular domains,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications The emerging field of signal processing on graphs: Ex- tending high-dimensional data analysis to networks and other irregular domains,

Reference 12

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Observation 6becdf5d-2603-45b0-b099-ac656c0a3df1 · outbound

This paper cites Approximating signals supported on graphs,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Approximating signals supported on graphs,

Reference 13

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This paper cites Geometric deep learning: going beyond euclidean data,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Geometric deep learning: going beyond euclidean data,

Reference 14

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Observation c40119af-8ac2-45dc-9a31-c83e6c3eddc5 · outbound

This paper cites A comprehensive survey on graph neural networks,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications A comprehensive survey on graph neural networks,

Reference 15

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Observation 503cd291-9d8a-4a65-a071-dd5e316aeadc · outbound

This paper cites Message passing neural networks,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Message passing neural networks,

Reference 16

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Observation 08a5698d-b4a1-48f2-afce-de9ef2942da6 · outbound

This paper cites Attention is all you need,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Attention is all you need,

Reference 17

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Observation 9843dca0-260a-4b74-a0d2-c6720c8752f6 · outbound

This paper cites Graph-based prediction of protein-protein interactions with attributed signed graph embedding,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Graph-based prediction of protein-protein interactions with attributed signed graph embedding,

Reference 18

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Observation e0d925d6-a469-411a-bd58-c5a70e76fd3e · outbound

This paper cites Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega,

Reference 19

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Observation ce41c402-df1e-4362-968c-a8e52c7d50b9 · outbound

This paper cites Practical con- struction of k-nearest neighbor graphs in metric spaces,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Practical con- struction of k-nearest neighbor graphs in metric spaces,

Reference 20

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Observation 95897e80-66b7-43d3-9179-bf4bafd7a127 · outbound

This paper cites Predicting protein-protein interactions based only on sequences infor- mation,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Predicting protein-protein interactions based only on sequences infor- mation,

Reference 21

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Observation b696ebde-3696-4632-9081-d799c8cb4fd0 · outbound

This paper cites exp2go: Improving prediction of functions in the gene ontology with expression data,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications exp2go: Improving prediction of functions in the gene ontology with expression data,

Reference 22

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Observation 88e65454-1634-4c01-8346-7af2bbb4a660 · outbound

This paper cites Cluster analysis and display of genome-wide expression patterns,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Cluster analysis and display of genome-wide expression patterns,

Reference 23

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A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Interaction with diurnal and circadian regulation results in dynamic metabolic and transcriptional changes during cold acclimation in arabidopsis,

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This paper cites dictybase: a new dictyostelium discoideum genome database,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications dictybase: a new dictyostelium discoideum genome database,

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This paper cites The cafa challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications The cafa challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens,

Reference 26

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A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Neural model-based similarity prediction for compounds with unknown structures,

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A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Evaluaci ´on de un modelo neuronal para la estimaci ´on de similaridad entre compuestos a partir de representaciones one-hot,

Reference 28

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A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Reopti- mization of mdl keys for use in drug discovery,

Reference 29

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This paper cites Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Reference 30

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This paper cites Multifaceted protein–protein interaction prediction based on siamese residual rcnn,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Multifaceted protein–protein interaction prediction based on siamese residual rcnn,

Reference 31

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This paper cites Protein interaction network reconstruction through ensemble deep learning with attention mechanism,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Protein interaction network reconstruction through ensemble deep learning with attention mechanism,

Reference 32

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This paper cites Benchmark evaluation of protein–protein interaction prediction algorithms,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Benchmark evaluation of protein–protein interaction prediction algorithms,

Reference 33

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Observation 6f4f3c8e-2b8c-4cb6-aace-311f1083b4ef · outbound

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A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications A large-scale evaluation of computational protein function prediction,

Reference 34

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This paper cites Basic local alignment search tool,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Basic local alignment search tool,

Reference 35

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Observation 43ce62d2-0b34-4522-9852-0963d225943f · outbound

This paper cites Nmfgo: Gene function prediction via nonnegative matrix factorization with gene ontology,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Nmfgo: Gene function prediction via nonnegative matrix factorization with gene ontology,

Reference 36

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Observation 0626b78b-e67c-44c2-a5d1-61274c4c74ef · outbound

This paper cites Deepgoplus: improved protein function prediction from sequence,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Deepgoplus: improved protein function prediction from sequence,

Reference 37

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Observation 5da462c8-dabb-492d-84a3-4c3a6bde8de3 · outbound

This paper cites Statistical comparisons of classifiers over multiple data sets,.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Statistical comparisons of classifiers over multiple data sets,

Reference 38

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Observation f8b77f5d-f1ca-4baa-ab5c-5795e0c84d51 · outbound

This paper cites Available: https://pubmed.ncbi.nlm.nih.gov/35011283/.

A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications Available: https://pubmed.ncbi.nlm.nih.gov/35011283/

Reference 2022

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.