Paper Citation Record · LEDGER

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups

As of 17 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2502.08432.

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

pith.paper-citation-record.v1
2502.08432 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:10:55.620829Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

45 of 45 outbound references displayed

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verified fuzzy34
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a81f0b9-37be-43a6-9ae0-63d8f77cd16b · outbound

This paper cites Uci machine learning repository,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Uci machine learning repository,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.091228Z

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Observation b8759802-dc2f-4da8-9b9a-44b43bb9ac01 · outbound

This paper cites Hypergraph convolution and hyper- graph attention,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hypergraph convolution and hyper- graph attention,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.080765Z

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Observation db02e569-6c05-4add-959d-d5eb329dc93b · outbound

This paper cites On visual similarity based 3d model retrieval,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups On visual similarity based 3d model retrieval,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.068771Z

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Observation 3ecac13b-3550-4d87-9650-f84aaaaf5a38 · outbound

This paper cites You are allset: A multiset function framework for hypergraph neural networks,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups You are allset: A multiset function framework for hypergraph neural networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.056898Z

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Observation bf78befa-21b5-4e1d-88db-3a8b58469c9e · outbound

This paper cites Fast and accurate deep network learning by exponential linear units (elus),.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Fast and accurate deep network learning by exponential linear units (elus),

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.045429Z

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Observation b3611491-a3e2-41fe-8dea-e71d3d32a53d · outbound

This paper cites Hnhn: Hypergraph networks with hyperedge neurons,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hnhn: Hypergraph networks with hyperedge neurons,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.033171Z

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Observation e14d7ef4-3e35-4f98-a854-7c72af76fa8f · outbound

This paper cites Hypergraph neural net- works,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hypergraph neural net- works,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.020280Z

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Observation 30ace370-5e47-4bf5-9c5e-03a887bda487 · outbound

This paper cites Quantum experiments and hypergraphs: Multiphoton sources for quantum interference, quantum computation, and quantum entanglement,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Quantum experiments and hypergraphs: Multiphoton sources for quantum interference, quantum computation, and quantum entanglement,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:56.008303Z

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Observation 79c8f2ec-5edf-4425-8d41-0b9617a5225d · outbound

This paper cites Contrastive multi-view represen- tation learning on graphs,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Contrastive multi-view represen- tation learning on graphs,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.991790Z

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Observation ed512672-a7af-420b-92f3-2ea568146406 · outbound

This paper cites Robust basket recommendation via noise- tolerated graph contrastive learning,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Robust basket recommendation via noise- tolerated graph contrastive learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.977749Z

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Observation e2198d34-d45a-48ab-8f53-a6594be615e6 · outbound

This paper cites Unignn: a unified framework for graph and hypergraph neural networks,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Unignn: a unified framework for graph and hypergraph neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.964886Z

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Observation bcb2ad1a-57c3-40de-89ae-3fff67ff193d · outbound

This paper cites I’m me, we’re us, and i’m us: Tri-directional contrastive learning on hypergraphs,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups I’m me, we’re us, and i’m us: Tri-directional contrastive learning on hypergraphs,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.952587Z

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Observation 2f906f1e-80a1-4a9a-883a-13406554589f · outbound

This paper cites Coclep: Contrastive learning-based semi-supervised community search,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Coclep: Contrastive learning-based semi-supervised community search,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.930731Z

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Observation 566892dd-3bc6-4b2d-a098-0bbf1d24bacc · outbound

This paper cites Hyperbolic hypergraphs for sequential recommendation,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hyperbolic hypergraphs for sequential recommendation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.920455Z

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Observation 3cc1775d-167a-430b-b23b-454a52e4d83c · outbound

This paper cites Generating 3D Molecules for Target Protein Binding.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Generating 3D Molecules for Target Protein Binding

Reference 15

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Observation c59ab67d-df23-46aa-921f-ee2d71d13183 · outbound

This paper cites Self-supervised dynamic hypergraph recommendation based on hyper-relational knowl- edge graph,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Self-supervised dynamic hypergraph recommendation based on hyper-relational knowl- edge graph,

Reference 16

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Observation 6fd4435d-24b3-47a4-95b2-175de1ecd3d8 · outbound

This paper cites Decoupled weight decay regularization,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Decoupled weight decay regularization,

Reference 17

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Observation 9445e44a-45ec-43b9-aa83-62cb6cffa5ac · outbound

This paper cites Dualgraph: Improving semi-supervised graph classification via dual contrastive learning,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Dualgraph: Improving semi-supervised graph classification via dual contrastive learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.898976Z

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Observation 50db0c62-ec33-4f34-86a1-a01bd2184aeb · outbound

This paper cites Graph representation learning via graphical mutual information maxi- mization,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Graph representation learning via graphical mutual information maxi- mization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.889227Z

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Observation 1011e946-b75a-4778-a260-c3e53220ce29 · outbound

This paper cites The network data repository with interactive graph analytics and visualization,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups The network data repository with interactive graph analytics and visualization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.878129Z

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Observation b30970ff-1a1b-4569-a17d-b52338085571 · outbound

This paper cites Hygnn: Drug-drug interaction prediction via hypergraph neural network,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hygnn: Drug-drug interaction prediction via hypergraph neural network,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.867324Z

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Observation a1616c7f-4ddd-4c9b-95db-517cca09adb6 · outbound

This paper cites Seq- hygan: Sequence classification via hypergraph attention network,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Seq- hygan: Sequence classification via hypergraph attention network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.856389Z

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Observation 44c2d43d-4008-4f4d-bb10-ac7413616f00 · outbound

This paper cites Collective classification in network data,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Collective classification in network data,

Reference 23

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Observation 8fc05fb4-f853-4b35-b8c3-e0078bbc4243 · outbound

This paper cites InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Reference 24

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Observation 9e537612-7c7f-4fb1-a60b-6d47f5cd6cf7 · outbound

This paper cites Ms-hgat: memory- enhanced sequential hypergraph attention network for information dif- fusion prediction,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Ms-hgat: memory- enhanced sequential hypergraph attention network for information dif- fusion prediction,

Reference 25

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Observation 2ad9bc6b-cced-4bd1-9c58-f242f48c8877 · outbound

This paper cites Self- supervised hypergraph representation learning for sociological analysis,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Self- supervised hypergraph representation learning for sociological analysis,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.834515Z

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Observation 84ddefef-d6c7-4f29-9bb1-b2997ac1cb84 · outbound

This paper cites Large-scale representation learning on graphs via bootstrapping,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Large-scale representation learning on graphs via bootstrapping,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.824413Z

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Observation 37aaf020-f858-4143-860c-9ab1307da5e0 · outbound

This paper cites Deep graph infomax,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Deep graph infomax,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.814970Z

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Observation 570a4880-1974-4705-8efe-f35bfbfe41b5 · outbound

This paper cites Hypertenet: Hypergraph and transformer-based neural network for personalized list continuation,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hypertenet: Hypergraph and transformer-based neural network for personalized list continuation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.805650Z

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Observation f3437e5f-ca0d-433e-b3c0-d8fefdd2607d · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups 3d shapenets: A deep representation for volumetric shapes,

Reference 30

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This paper cites Self-supervised hypergraph convolutional networks for session-based recommendation,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Self-supervised hypergraph convolutional networks for session-based recommendation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.791045Z

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Observation 4ae626d0-c208-4c9a-b050-455c947d0c73 · outbound

This paper cites Contrastive learning for sequential recommendation,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Contrastive learning for sequential recommendation,

Reference 32

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Observation 0ec3877a-ee49-47e0-ba8e-dcc836e7436c · outbound

This paper cites Hypergcn: A new method for training graph convolutional networks on hypergraphs,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Hypergcn: A new method for training graph convolutional networks on hypergraphs,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.775994Z

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Observation 19acea5d-6e83-4cb8-a7be-dead99f97dff · outbound

This paper cites Semi-supervised hyper- graph node classification on hypergraph line expansion,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Semi-supervised hyper- graph node classification on hypergraph line expansion,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.766526Z

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Observation 3c37ba8d-a1bf-42fd-8f1a-81db3f0d36a1 · outbound

This paper cites Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach,

Reference 35

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This paper cites Group identification via transitional hypergraph convolution with cross-view self-supervised learning,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Group identification via transitional hypergraph convolution with cross-view self-supervised learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.750306Z

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Observation 52508147-3f67-4b7b-b05c-1cdc063e9e23 · outbound

This paper cites Dynamic hypergraph convolutional network,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Dynamic hypergraph convolutional network,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.739812Z

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Observation 08ecd316-d759-4c9f-9887-70b8f693a2b9 · outbound

This paper cites Graph con- trastive learning with augmentations,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Graph con- trastive learning with augmentations,

Reference 38

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This paper cites Cross-modality and self-supervised protein em- bedding for compound–protein affinity and contact prediction,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Cross-modality and self-supervised protein em- bedding for compound–protein affinity and contact prediction,

Reference 39

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Observation a1e51f85-7818-4570-b566-eca9b6b92971 · outbound

This paper cites Double-scale self-supervised hypergraph learning for group recommendation,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Double-scale self-supervised hypergraph learning for group recommendation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.719455Z

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Observation 85dc4972-925b-4668-b942-f13eee17d000 · outbound

This paper cites Multiscale and integrative single-cell hi-c analysis with higashi,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Multiscale and integrative single-cell hi-c analysis with higashi,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.709466Z

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Observation 5dde3cfe-a704-4a24-b314-e5fa17f5d87f · outbound

This paper cites Graph debiased contrastive learning with joint representation clustering.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Graph debiased contrastive learning with joint representation clustering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.699816Z

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Observation 3447fb37-ae8f-4b05-bc74-d5afb0dc6636 · outbound

This paper cites Deep graph con- trastive representation learning,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Deep graph con- trastive representation learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.689513Z

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Observation 39e9538a-803f-4b32-8ebc-2594c0c7e5da · outbound

This paper cites Graph contrastive learning with adaptive augmentation,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Graph contrastive learning with adaptive augmentation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.679586Z

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Observation 29ef3011-a450-4ef0-8c2f-b96cb15cb7b7 · outbound

This paper cites Inhomogeneous social recommendation with hypergraph convolutional networks,.

Closer through commonality: Enhancing hypergraph contrastive learning with shared groups Inhomogeneous social recommendation with hypergraph convolutional networks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:10:55.668810Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.