Paper Citation Record · LEDGER

Data-Driven Self-Supervised Graph Representation Learning

As of 21 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2412.18316.

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

pith.paper-citation-record.v1
2412.18316 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:52:08.589685Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation 291c34e7-c311-4962-89a0-77ff2d45c812 · outbound

This paper cites Vicreg: V ariance-invariance- covariance regularization for self-supervised learning, 2021.

Data-Driven Self-Supervised Graph Representation Learning Vicreg: V ariance-invariance- covariance regularization for self-supervised learning, 2021

Reference 1

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Data-Driven Self-Supervised Graph Representation Learning Unresolved cited work

Reference 2

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Data-Driven Self-Supervised Graph Representation Learning Unresolved cited work

Reference 3

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This paper cites Unsupervised Learning of Visual Features by Contrasting Cluster Assignments.

Data-Driven Self-Supervised Graph Representation Learning Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Reference 4

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Observation fe4f6d85-35f2-46c8-a1f9-3535e7c7c1aa · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Data-Driven Self-Supervised Graph Representation Learning Emerging Properties in Self-Supervised Vision Transformers

Reference 5

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Observation 1af2adbd-96b4-4762-8442-98593f4b9a36 · outbound

This paper cites Exploring Simple Siamese Representation Learning.

Data-Driven Self-Supervised Graph Representation Learning Exploring Simple Siamese Representation Learning

Reference 6

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Observation c135c3bd-3745-460c-8eef-7f39c5142f8e · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.

Data-Driven Self-Supervised Graph Representation Learning Bert: Pre-training of deep bidirectional transformers for language understanding, 2019

Reference 7

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Observation 8358c90b-c7fb-4c2d-89c8-772014db9ff2 · outbound

This paper cites Chawla, and Ananthram Swami, ‘Metapath2vec: Scalable representation learning for heterogeneous networks’, inProceedings of the 23rd ACM SIGKDD, KDD ’17, p.

Data-Driven Self-Supervised Graph Representation Learning Chawla, and Ananthram Swami, ‘Metapath2vec: Scalable representation learning for heterogeneous networks’, inProceedings of the 23rd ACM SIGKDD, KDD ’17, p

Reference 8

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This paper cites A fair comparison of graph neural networks for graph classification, 2020.

Data-Driven Self-Supervised Graph Representation Learning A fair comparison of graph neural networks for graph classification, 2020

Reference 9

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Observation 583e8603-601b-40fb-a8ea-e241b2538372 · outbound

This paper cites ACM, (apr 2020).

Data-Driven Self-Supervised Graph Representation Learning ACM, (apr 2020)

Reference 10

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Observation 08adc405-1494-4f4b-a845-462959a5673d · outbound

This paper cites Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal V alko.

Data-Driven Self-Supervised Graph Representation Learning Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal V alko

Reference 11

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Observation 2d40295e-1055-40fe-8d98-58d2c61b1c76 · outbound

This paper cites node2vec: Scalable feature learning for networks, 2016.

Data-Driven Self-Supervised Graph Representation Learning node2vec: Scalable feature learning for networks, 2016

Reference 12

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Observation 0aaedee9-8b43-4e3c-83ed-6eef4ed1b4e3 · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

Data-Driven Self-Supervised Graph Representation Learning Hamilton, Rex Ying, and Jure Leskovec

Reference 13

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Observation a14e81e1-0fa2-4706-b040-a02bbc035f6f · outbound

This paper cites Contrastive Multi-View Representation Learning on Graphs.

Data-Driven Self-Supervised Graph Representation Learning Contrastive Multi-View Representation Learning on Graphs

Reference 14

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Observation d3e07020-31ab-4bd6-9ccb-1260f3a94202 · outbound

This paper cites Girshick, ‘Momentum contrast for unsupervised visual representation learning’, (2019).

Data-Driven Self-Supervised Graph Representation Learning Girshick, ‘Momentum contrast for unsupervised visual representation learning’, (2019)

Reference 15

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Observation 17e04b63-aa42-4bd2-82d3-1fefecd1edd1 · outbound

This paper cites 2414–2424, (2021).

Data-Driven Self-Supervised Graph Representation Learning 2414–2424, (2021)

Reference 16

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Observation e01bffe8-3c80-4f27-a230-fdff06ca6c9c · outbound

This paper cites Kefato and Sarunas Girdzijauskas, ‘Gossip and attend: Context-sensitive graph representation learning’, in14-th International Conference on W eb and Social Media, ICWSM’20, (2020).

Data-Driven Self-Supervised Graph Representation Learning Kefato and Sarunas Girdzijauskas, ‘Gossip and attend: Context-sensitive graph representation learning’, in14-th International Conference on W eb and Social Media, ICWSM’20, (2020)

Reference 17

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Observation caf66fd0-fae3-4efb-ba4b-e3cf47199101 · outbound

This paper cites Kefato and Sarunas Girdzijauskas, ‘Self-supervised graph neural networks without explicit negative sampling’,CoRR, (2021).

Data-Driven Self-Supervised Graph Representation Learning Kefato and Sarunas Girdzijauskas, ‘Self-supervised graph neural networks without explicit negative sampling’,CoRR, (2021)

Reference 18

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Observation 92885f68-4be3-4693-8d51-8c43dbef96f1 · outbound

This paper cites Kipf and Max Welling.

Data-Driven Self-Supervised Graph Representation Learning Kipf and Max Welling

Reference 19

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This paper cites Diffusion improves graph learning, 2019.

Data-Driven Self-Supervised Graph Representation Learning Diffusion improves graph learning, 2019

Reference 20

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Observation ad26e791-eb46-46a7-b897-4f161029ade5 · outbound

This paper cites Albert: A lite bert for self-supervised learning of language representations, 2020.

Data-Driven Self-Supervised Graph Representation Learning Albert: A lite bert for self-supervised learning of language representations, 2020

Reference 21

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Data-Driven Self-Supervised Graph Representation Learning Unresolved cited work

Reference 22

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Observation eba21460-84bc-40c9-839a-c29a3390db05 · outbound

This paper cites Roberta: A robustly optimized bert pretraining approach, 2019.

Data-Driven Self-Supervised Graph Representation Learning Roberta: A robustly optimized bert pretraining approach, 2019

Reference 23

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Reference 24

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Observation 4495d539-6e82-4ba2-80b7-e942a586b4c8 · outbound

This paper cites 5371–5378, (2020).

Data-Driven Self-Supervised Graph Representation Learning 5371–5378, (2020)

Reference 25

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Observation 874bbbe2-2b89-416e-be5f-49d6461db63a · outbound

This paper cites 701–710, New Y ork, NY , USA, (2014).

Data-Driven Self-Supervised Graph Representation Learning 701–710, New Y ork, NY , USA, (2014)

Reference 26

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Observation abdf28c1-738f-4900-ae0b-dae753ed6e86 · outbound

This paper cites Heterogeneous Deep Graph Infomax.

Data-Driven Self-Supervised Graph Representation Learning Heterogeneous Deep Graph Infomax

Reference 27

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This paper cites Multi-scale attributed node embedding, 2021.

Data-Driven Self-Supervised Graph Representation Learning Multi-scale attributed node embedding, 2021

Reference 28

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Observation 8c4be315-33b6-4b93-9c6c-8fba17496f94 · outbound

This paper cites 1157–1166, (2022).

Data-Driven Self-Supervised Graph Representation Learning 1157–1166, (2022)

Reference 29

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Observation 64ea6c4e-5877-48e8-99c5-de91aa679938 · outbound

This paper cites Kaplan, Jiawei Han, and Jian Peng.

Data-Driven Self-Supervised Graph Representation Learning Kaplan, Jiawei Han, and Jian Peng

Reference 30

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Observation 09beef0f-b639-4aba-914b-8f2c063f3460 · outbound

This paper cites Pitfalls of graph neural network evaluation, 2019.

Data-Driven Self-Supervised Graph Representation Learning Pitfalls of graph neural network evaluation, 2019

Reference 31

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Reference 32

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Reference 33

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Observation c79f067e-79e1-4bf4-9001-5c64ca80e7b5 · outbound

This paper cites Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge, 2021.

Data-Driven Self-Supervised Graph Representation Learning Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge, 2021

Reference 34

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Observation a056dd5d-0b0e-4840-becf-93ea4faf7837 · outbound

This paper cites Adversarial graph augmentation to improve graph contrastive learning, 2021.

Data-Driven Self-Supervised Graph Representation Learning Adversarial graph augmentation to improve graph contrastive learning, 2021

Reference 35

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Reference 36

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Observation 02ec0b13-f3ea-473c-83c5-b2f132629ade · outbound

This paper cites 1067–1077, Republic and Canton of Geneva, CHE, (2015).

Data-Driven Self-Supervised Graph Representation Learning 1067–1077, Republic and Canton of Geneva, CHE, (2015)

Reference 37

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Observation 07d75101-cb0b-4fdd-95dd-9e2b5c9e48b6 · outbound

This paper cites Bootstrapped representation learning on graphs, 2021.

Data-Driven Self-Supervised Graph Representation Learning Bootstrapped representation learning on graphs, 2021

Reference 38

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Observation 1c1b817a-6d13-4cc2-9d11-86450badbfe2 · outbound

This paper cites Augmentations in graph contrastive learning: Current methodological flaws; towards better practices, 2021.

Data-Driven Self-Supervised Graph Representation Learning Augmentations in graph contrastive learning: Current methodological flaws; towards better practices, 2021

Reference 39

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This paper cites Hamilton, Pietro Liò, Y oshua Bengio, and R Devon Hjelm.

Data-Driven Self-Supervised Graph Representation Learning Hamilton, Pietro Liò, Y oshua Bengio, and R Devon Hjelm

Reference 40

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Observation 46a65ebf-a794-4132-b4fd-3ece28d7844d · outbound

This paper cites 1631–1639, (2022).

Data-Driven Self-Supervised Graph Representation Learning 1631–1639, (2022)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:52:08.739013Z

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Observation 91a09f9b-afff-4ff7-bcf9-f2e97ae19328 · outbound

This paper cites 2022–2032, New Y ork, NY , USA, (2019).

Data-Driven Self-Supervised Graph Representation Learning 2022–2032, New Y ork, NY , USA, (2019)

Reference 42

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This paper cites Self-supervised heterogeneous graph neural network with co-contrastive learning, 2021.

Data-Driven Self-Supervised Graph Representation Learning Self-supervised heterogeneous graph neural network with co-contrastive learning, 2021

Reference 43

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This paper cites 1726–1736, (2021).

Data-Driven Self-Supervised Graph Representation Learning 1726–1736, (2021)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:52:08.715442Z

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Observation 77f1a519-753d-4db4-a361-03f54af9625d · outbound

This paper cites 1070–1079, (2022).

Data-Driven Self-Supervised Graph Representation Learning 1070–1079, (2022)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:52:08.707963Z

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Observation 6eb54195-5547-4192-b564-b497f3b1b948 · outbound

This paper cites Self-supervised learning of graph neural networks: A unified review, 2021.

Data-Driven Self-Supervised Graph Representation Learning Self-supervised learning of graph neural networks: A unified review, 2021

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:52:08.700394Z

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Observation 8f4a9a6e-0451-4d8e-b873-f01b025c72be · outbound

This paper cites Graph Contrastive Learning Automated.

Data-Driven Self-Supervised Graph Representation Learning Graph Contrastive Learning Automated

Reference 47

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Reference 48

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Reference 49

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Observation 800f5800-aec8-4414-94f4-75c5dd51c65c · outbound

This paper cites Graphsaint: Graph sampling based inductive learning method, 2020.

Data-Driven Self-Supervised Graph Representation Learning Graphsaint: Graph sampling based inductive learning method, 2020

Reference 50

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Reference 51

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Observation a21659df-a08d-40a6-bcb7-2f2ca955f5d9 · outbound

This paper cites Deep graph contrastive representation learning, 2020.

Data-Driven Self-Supervised Graph Representation Learning Deep graph contrastive representation learning, 2020

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:52:08.664834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a8c7b42-2bde-4f15-80c1-7727021645a2 · outbound

This paper cites 2069–2080, New Y ork, NY , USA, (2021).

Data-Driven Self-Supervised Graph Representation Learning 2069–2080, New Y ork, NY , USA, (2021)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:52:08.656168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.