Paper Citation Record · LEDGER

Applications of temporal graph learning for predicting the dynamics of biological systems

As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2605.28659.

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

pith.paper-citation-record.v1
2605.28659 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T13:56:45.020535Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

35 of 35 outbound references displayed

verified exact0
verified fuzzy0
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c164c2ba-d1b1-4615-afc4-417fc6b35443 · outbound

This paper cites scgpt:towardbuildingafoundationmodelforsingle-cellmulti-omics using generative ai.Nature Methods, 21(8):1470–1480, Aug 2024.

Applications of temporal graph learning for predicting the dynamics of biological systems scgpt:towardbuildingafoundationmodelforsingle-cellmulti-omics using generative ai.Nature Methods, 21(8):1470–1480, Aug 2024

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdd59af5-c438-47a9-a1fa-d5de99794255 · outbound

This paper cites Large-scale foundation model on single-cell transcriptomics.Nature Methods, 21(8):1481–1491, Aug 2024.

Applications of temporal graph learning for predicting the dynamics of biological systems Large-scale foundation model on single-cell transcriptomics.Nature Methods, 21(8):1481–1491, Aug 2024

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9438b3bb-1812-4ea3-bd75-c1ccb3736a6e · outbound

This paper cites Theodoris, Ling Xiao, Anant Chopra, Mark D.

Applications of temporal graph learning for predicting the dynamics of biological systems Theodoris, Ling Xiao, Anant Chopra, Mark D

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc83fbb6-d5ef-4d79-a62c-0589738f34e1 · outbound

This paper cites Alexander Wolf, and Fabian J.

Applications of temporal graph learning for predicting the dynamics of biological systems Alexander Wolf, and Fabian J

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b15bef7e-3fa2-4182-97c0-1d5a71ff8db1 · outbound

This paper cites Alexander Wolf, Florian Buettner, and Fabian J.

Applications of temporal graph learning for predicting the dynamics of biological systems Alexander Wolf, Florian Buettner, and Fabian J

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee467f78-8398-4f16-a751-2439b30d99b2 · outbound

This paper cites Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities.Transactions on Machine Learning Research, 2023.

Applications of temporal graph learning for predicting the dynamics of biological systems Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities.Transactions on Machine Learning Research, 2023

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee521ed6-88d5-499c-9254-acce88f52ae3 · outbound

This paper cites Dileo et al.

Applications of temporal graph learning for predicting the dynamics of biological systems Dileo et al

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b66f0fed-88d9-439c-a708-f57190d76182 · outbound

This paper cites Burkhardt, Andrea Califano, Jonah Cool, Abby F.

Applications of temporal graph learning for predicting the dynamics of biological systems Burkhardt, Andrea Califano, Jonah Cool, Abby F

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92b7ce65-e03b-403b-aa19-867d9e372285 · outbound

This paper cites Roohani, Tony J.

Applications of temporal graph learning for predicting the dynamics of biological systems Roohani, Tony J

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b496ea4-966f-41fa-8449-0e0708b450bc · outbound

This paper cites Predicting transcriptional outcomes of novel multigene perturbations with gears.Nature Biotechnology, 42(6):927–935, Jun 2024.

Applications of temporal graph learning for predicting the dynamics of biological systems Predicting transcriptional outcomes of novel multigene perturbations with gears.Nature Biotechnology, 42(6):927–935, Jun 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c19afe0-991f-4517-8cef-60fc3f8aca80 · outbound

This paper cites HEIST: A graph foundation model for spatial transcriptomics and proteomics data.

Applications of temporal graph learning for predicting the dynamics of biological systems HEIST: A graph foundation model for spatial transcriptomics and proteomics data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7638cf4-05b3-4fa9-a866-16911869fa9e · outbound

This paper cites Temporal Graph Benchmark for Machine Learning on Temporal Graphs.

Applications of temporal graph learning for predicting the dynamics of biological systems Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:03:29.631519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fab89e99-518a-4d5c-866d-07a6106af941 · outbound

This paper cites Evolvegcn: Evolving graph convolutional networks for dynamic graphs.

Applications of temporal graph learning for predicting the dynamics of biological systems Evolvegcn: Evolving graph convolutional networks for dynamic graphs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfd6a579-354e-4bf3-a5f9-b116d581e6cc · outbound

This paper cites Structured sequence modeling with graph convolutional recurrent networks.

Applications of temporal graph learning for predicting the dynamics of biological systems Structured sequence modeling with graph convolutional recurrent networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b67e4d76-582e-47e9-98dc-d8d28c17f2d4 · outbound

This paper cites Roland: Graph learning framework for dynamic graphs.

Applications of temporal graph learning for predicting the dynamics of biological systems Roland: Graph learning framework for dynamic graphs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac422ac4-d27d-4763-a5dd-baf31f1572cc · outbound

This paper cites Input snapshots fusion for scalable discrete-time dynamic graph neural networks.

Applications of temporal graph learning for predicting the dynamics of biological systems Input snapshots fusion for scalable discrete-time dynamic graph neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df3b97bb-0c45-4c66-bb8a-dcec791b1992 · outbound

This paper cites Modelling and analysis of gene regulatory net- works.Nature Reviews Molecular Cell Biology, 9(10):770–780, Oct 2008.

Applications of temporal graph learning for predicting the dynamics of biological systems Modelling and analysis of gene regulatory net- works.Nature Reviews Molecular Cell Biology, 9(10):770–780, Oct 2008

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad4116c8-2d85-4e2a-b074-56f298f1aaee · outbound

This paper cites Springer New York, New York, NY, 2019.

Applications of temporal graph learning for predicting the dynamics of biological systems Springer New York, New York, NY, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7adae8e5-50f5-4016-80fd-bdebba32f64c · outbound

This paper cites Deep learning in gene regulatory network in- ference: A survey.IEEE/ACM Transactions on Computational Biology and Bioin- formatics, 21(6):2089–2101, 2024.

Applications of temporal graph learning for predicting the dynamics of biological systems Deep learning in gene regulatory network in- ference: A survey.IEEE/ACM Transactions on Computational Biology and Bioin- formatics, 21(6):2089–2101, 2024

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 485967f2-4965-4009-9c2a-d8243f6b7ee7 · outbound

This paper cites da Silva, Heder S.

Applications of temporal graph learning for predicting the dynamics of biological systems da Silva, Heder S

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff056eb1-92be-48ca-9ee3-3595db0ed3bc · outbound

This paper cites Cambridge University Press, Cambridge, 2016.

Applications of temporal graph learning for predicting the dynamics of biological systems Cambridge University Press, Cambridge, 2016

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d305dd43-2b1b-4cc3-b91e-0ec0251f16fe · outbound

This paper cites Lichtenwalter, and Nitesh V.

Applications of temporal graph learning for predicting the dynamics of biological systems Lichtenwalter, and Nitesh V

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06222036-954b-45a9-8b72-bf18f71738ee · outbound

This paper cites Temporal graph networks for deep learning on dynamic graphs, 2020.

Applications of temporal graph learning for predicting the dynamics of biological systems Temporal graph networks for deep learning on dynamic graphs, 2020

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ffde2eb-bd90-417a-9dab-72fd28d350b5 · outbound

This paper cites Towards better evaluation for dynamic link prediction.

Applications of temporal graph learning for predicting the dynamics of biological systems Towards better evaluation for dynamic link prediction

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b5f4f86-a644-4c1b-a8b6-c23f7959b42a · outbound

This paper cites Kipf and Max Welling.

Applications of temporal graph learning for predicting the dynamics of biological systems Kipf and Max Welling

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f88ff19c-ab53-4097-845a-fac9cf6eb714 · outbound

This paper cites Graph attention networks.

Applications of temporal graph learning for predicting the dynamics of biological systems Graph attention networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0115ecf0-84ae-4811-9c0f-cef491165cb5 · outbound

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

Applications of temporal graph learning for predicting the dynamics of biological systems Convolutional neural networks on graphs with fast localized spectral filtering

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2874163-7cb5-4512-978e-8206d3df6450 · outbound

This paper cites Towards better dynamic graph learning: New architecture and unified library.

Applications of temporal graph learning for predicting the dynamics of biological systems Towards better dynamic graph learning: New architecture and unified library

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a800fe51-b28f-4c92-9031-5c2911066a96 · outbound

This paper cites Griffiths, Carolina Guibentif, Tom W.

Applications of temporal graph learning for predicting the dynamics of biological systems Griffiths, Carolina Guibentif, Tom W

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67f5d261-0e84-4c19-a45a-edfa36cfc756 · outbound

This paper cites Comprehensive single cell mrna profiling reveals a detailed roadmap for pancreatic endocrinogenesis.Development, 146(12):dev173849, 06 2019.

Applications of temporal graph learning for predicting the dynamics of biological systems Comprehensive single cell mrna profiling reveals a detailed roadmap for pancreatic endocrinogenesis.Development, 146(12):dev173849, 06 2019

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86803f31-36b1-4270-9356-8c50dc5f3b4c · outbound

This paper cites Oversmoothing, ”oversquashing”, heterophily, long-range, and more: Demystifying common beliefs in graph machine learning.

Applications of temporal graph learning for predicting the dynamics of biological systems Oversmoothing, ”oversquashing”, heterophily, long-range, and more: Demystifying common beliefs in graph machine learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef8d38fa-6cc1-4ae0-a481-aacb378883a2 · outbound

This paper cites Erythroid Krüppel-Like factor (KLF1): A surprisingly versatile regulator of erythroid differentiation.Adv Exp Med Biol, 1459:217–242, 2024.

Applications of temporal graph learning for predicting the dynamics of biological systems Erythroid Krüppel-Like factor (KLF1): A surprisingly versatile regulator of erythroid differentiation.Adv Exp Med Biol, 1459:217–242, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74acba70-a926-420f-9fa0-8c932b0e26d2 · outbound

This paper cites Scott, M.

Applications of temporal graph learning for predicting the dynamics of biological systems Scott, M

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24adeb1c-34aa-4c45-9fce-85fa83241846 · outbound

This paper cites ERG dependence distinguishes developmental control of hematopoietic stem cell maintenance from hematopoietic specification.Genes Dev, 25(3):251–262, January 2011.

Applications of temporal graph learning for predicting the dynamics of biological systems ERG dependence distinguishes developmental control of hematopoietic stem cell maintenance from hematopoietic specification.Genes Dev, 25(3):251–262, January 2011

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bed6646a-5cd3-4058-9c2a-4539357c16f8 · outbound

This paper cites The home- odomain protein meis1 is essential for definitive hematopoiesis and vascular pat- terning in the mouse embryo.Dev Biol, 280(2):307–320, April 2005.

Applications of temporal graph learning for predicting the dynamics of biological systems The home- odomain protein meis1 is essential for definitive hematopoiesis and vascular pat- terning in the mouse embryo.Dev Biol, 280(2):307–320, April 2005

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-29T13:56:45.020535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.