Paper Citation Record · LEDGER

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks

As of 9 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2601.22427.

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

pith.paper-citation-record.v1
2601.22427 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T09:48:18.217276Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

33 of 33 outbound references displayed

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verified fuzzy31
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a9600b1-b16e-4432-a4b8-ff55dbe27a4b · outbound

This paper cites Expo- nentially improving the complexity of simulating the weisfeiler-lehman test with graph neural networks.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Expo- nentially improving the complexity of simulating the weisfeiler-lehman test with graph neural networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.603308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 03ed14c0-2fb7-4571-a9d9-9a448e6f92da · outbound

This paper cites Evolution- ary dynamics of higher-order interactions in social networks.Nature Human Behaviour, 5(5):586–595.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Evolution- ary dynamics of higher-order interactions in social networks.Nature Human Behaviour, 5(5):586–595

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.600429Z

Source-reported events for the cited work

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Observation 947358ac-d855-42ae-aa23-cc0e5a3f36ff · outbound

This paper cites Do we really need complicated model architectures for temporal networks? InInterna- tional Conference on Learning Representations.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Do we really need complicated model architectures for temporal networks? InInterna- tional Conference on Learning Representations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.581279Z

Source-reported events for the cited work

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Observation a19b8b01-cfbc-4b48-b8f2-4b42c2ac6eaa · outbound

This paper cites Dy- namic fraud detection: Integrating reinforcement learn- ing into graph neural networks.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Dy- namic fraud detection: Integrating reinforcement learn- ing into graph neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.588456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc7697f7-a0fe-43cd-adc9-5878e12c64b0 · outbound

This paper cites Counterfactual expla- nations and how to find them: literature review and benchmarking.Data Mining and Knowledge Discov- ery, 38(5):2770–2824.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Counterfactual expla- nations and how to find them: literature review and benchmarking.Data Mining and Knowledge Discov- ery, 38(5):2770–2824

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.597622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eb0c8727-ed0c-4c15-8930-34064c243540 · outbound

This paper cites Social recommendation via graph-level counterfactual aug- mentation.Proceedings of the AAAI Conference on Ar- tificial Intelligence, 39(1):334–342, Apr.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Social recommendation via graph-level counterfactual aug- mentation.Proceedings of the AAAI Conference on Ar- tificial Intelligence, 39(1):334–342, Apr

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.578332Z

Source-reported events for the cited work

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Observation 0f5fd9ff-e040-4526-956b-cdab6354cf87 · outbound

This paper cites Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.591959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 023a15cf-c202-4950-b9c4-ce20ae28d8be · outbound

This paper cites Representation learning for dy- namic graphs: A survey.J.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Representation learning for dy- namic graphs: A survey.J

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.594675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b0aaea05-bea8-4669-bb29-e82641c708b7 · outbound

This paper cites On generating plausible counterfactual and semi-factual explanations for deep learning.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks On generating plausible counterfactual and semi-factual explanations for deep learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.584614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 435ccd7e-81ab-41d2-b381-9b063fce2d38 · outbound

This paper cites Predicting dynamic embedding trajectory in temporal interaction networks.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Predicting dynamic embedding trajectory in temporal interaction networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.566349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d0cfaed5-9fb9-425b-aed4-bf5b64bb4656 · outbound

This paper cites Neighborhood- aware scalable temporal network representation learn- ing.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Neighborhood- aware scalable temporal network representation learn- ing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.560444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e3baf220-ad64-4053-aae0-884cde0e4de3 · outbound

This paper cites Streaming graph neural networks.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Streaming graph neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.563621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f0bd60dd-87f4-47b3-97d9-0580eeb2943c · outbound

This paper cites Clear: Genera- tive counterfactual explanations on graphs.Advances in neural information processing systems, 35:25895– 25907.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Clear: Genera- tive counterfactual explanations on graphs.Advances in neural information processing systems, 35:25895– 25907

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.569190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 87374040-8fe7-472b-b58c-e51478a8c09a · outbound

This paper cites Benchmarking counterfactual image genera- tion.Advances in Neural Information Processing Sys- tems, 37:133207–133230.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Benchmarking counterfactual image genera- tion.Advances in Neural Information Processing Sys- tems, 37:133207–133230

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.572439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0ea6e17a-a850-4c41-b0ab-705f4eb03a1a · outbound

This paper cites Towards better evaluation for dynamic link prediction.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Towards better evaluation for dynamic link prediction

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.575445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4ac6fb31-13d9-4200-b462-b4999359db45 · outbound

This paper cites Unifying evolution, explanation, and discernment: A generative approach for dynamic graph counterfactuals.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Unifying evolution, explanation, and discernment: A generative approach for dynamic graph counterfactuals

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.512257Z

Source-reported events for the cited work

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Observation c41ff04e-0dae-470f-9141-5193fd960b65 · outbound

This paper cites Cody: Counterfactual explainers for dynamic graphs.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Cody: Counterfactual explainers for dynamic graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.553933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16609a6e-9b94-4994-a73c-11a6d8caf1ea · outbound

This paper cites Temporal graph networks for deep learning on dynamic graphs.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Temporal graph networks for deep learning on dynamic graphs

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.515232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3740ec42-b3e0-45bc-b532-1e541de699b7 · outbound

This paper cites Causal inference using po- tential outcomes: Design, modeling, decisions.Journal of the American statistical Association, 100(469):322– 331.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Causal inference using po- tential outcomes: Design, modeling, decisions.Journal of the American statistical Association, 100(469):322– 331

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.547646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8bb2092b-24dd-4c51-aa1b-a4fe4ca91ac6 · outbound

This paper cites Counterfactual explana- tions can be manipulated.Advances in neural informa- tion processing systems, 34:62–75.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Counterfactual explana- tions can be manipulated.Advances in neural informa- tion processing systems, 34:62–75

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.538406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a31e9617-e457-4c89-999a-cca9c775eeb6 · outbound

This paper cites Learning and evaluating graph neural network explanations based on counterfactual and factual rea- soning.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Learning and evaluating graph neural network explanations based on counterfactual and factual rea- soning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.541571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c4bf0700-87f7-4816-bc6c-6b6d8c04aacb · outbound

This paper cites Freedyg: Frequency enhanced continuous-time dy- namic graph model for link prediction.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Freedyg: Frequency enhanced continuous-time dy- namic graph model for link prediction

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.544571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd76d9a0-dac5-4487-878a-69831b394fbd · outbound

This paper cites Dyrep: Learn- ing representations over dynamic graphs.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Dyrep: Learn- ing representations over dynamic graphs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.557211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4b802fe8-06e3-4dff-83f9-c678b1cc9ddc · outbound

This paper cites TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T09:50:49.297004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4169c079-3f0d-4345-b8be-dad28843aa41 · outbound

This paper cites Dynamic graph transformer with correlated spatial-temporal positional encoding.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Dynamic graph transformer with correlated spatial-temporal positional encoding

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.529526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation af5bb2ad-7c75-4dd1-9e18-218c734c5c8f · outbound

This paper cites Counter- factual data augmentation with denoising diffusion for graph anomaly detection.IEEE Transactions on Com- putational Social Systems, 11(6):7555–7567.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Counter- factual data augmentation with denoising diffusion for graph anomaly detection.IEEE Transactions on Com- putational Social Systems, 11(6):7555–7567

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.532711Z

Source-reported events for the cited work

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Observation ed9a1830-04d4-4bc3-ad5e-2282cd671000 · outbound

This paper cites Fac- tual and informative review generation for explainable recommendation.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Fac- tual and informative review generation for explainable recommendation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.520752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dfa354c5-5dee-4a01-aee9-31eb9268bb3a · outbound

This paper cites Inductive repre- sentation learning on temporal graphs.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Inductive repre- sentation learning on temporal graphs

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.523628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f7e2b13-6bd9-470d-866d-bf25dd9135ee · outbound

This paper cites Towards better dynamic graph learning: New archi- tecture and unified library.Advances in Neural Infor- mation Processing Systems, 36:67686–67700.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Towards better dynamic graph learning: New archi- tecture and unified library.Advances in Neural Infor- mation Processing Systems, 36:67686–67700

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.526724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1425e391-2f39-4100-80b7-8bc6b7ce337f · outbound

This paper cites In- ductive matrix completion based on graph neural net- works.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks In- ductive matrix completion based on graph neural net- works

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.509243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 31b50076-abd2-4435-88bd-0705ea4c13ad · outbound

This paper cites An attentional multi-scale co-evolving model for dynamic link prediction.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks An attentional multi-scale co-evolving model for dynamic link prediction

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.535384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e957dbcd-2e8d-4a3e-a639-dad5acb468d1 · outbound

This paper cites Learning from coun- terfactual links for link prediction.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Learning from coun- terfactual links for link prediction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T09:50:50.550773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0cbd4978-ad48-4c2b-96d5-98b3b5092814 · outbound

This paper cites an unresolved cited work.

CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-05-16T09:50:50.517829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

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