Paper Citation Record · LEDGER

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics

As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 2 inbound Pith citation observations for arXiv:2502.02975.

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

pith.paper-citation-record.v1
2502.02975 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:31:15.989021Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:10:34.172591Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T17:46:34.351768Z

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy16
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfee933f-957e-4a03-a596-8dbf04e794a6 · outbound

This paper cites Temporal graph neural networks for social recommendation.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Temporal graph neural networks for social recommendation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.676593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6ce3761f-3c7a-4bb6-ace5-823a6d936be4 · outbound

This paper cites TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.873805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c686732c-beda-4380-a4c0-a810d6dd3ece · outbound

This paper cites ISBN 9798400705052.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics ISBN 9798400705052

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.904850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536e6e0c-22fb-42cf-bded-1f3e567c2f01 · outbound

This paper cites Measurement and analysis of online social networks.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Measurement and analysis of online social networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.592851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 34b6c559-bf1d-4646-b17e-5ddd0e17ae23 · outbound

This paper cites Sentence-BERT: Sentence embeddings using Siamese BERT- networks.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Sentence-BERT: Sentence embeddings using Siamese BERT- networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.575494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation be967682-bfe3-479e-829a-ecb8eb1e6814 · outbound

This paper cites doi: 10.18653/v1/D19-1410.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics doi: 10.18653/v1/D19-1410

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.919735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e4ed55d-45c3-446c-a81b-1b41c05843ad · outbound

This paper cites URL https://doi.org/10.1609/aaai.v33i01.33014806.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics URL https://doi.org/10.1609/aaai.v33i01.33014806

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.924929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dac23ae4-d1ff-4a2d-b413-13b032b27b8d · outbound

This paper cites URL https://doi.org/10.1109/ACCESS.2021.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics URL https://doi.org/10.1109/ACCESS.2021

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.929924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aed66a5-1960-4f32-8fff-31f7078d405b · outbound

This paper cites Dyrep: Learning representations over dynamic graphs.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Dyrep: Learning representations over dynamic graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.560003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d62bfda-7449-4edb-a088-fb0e284984e5 · outbound

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

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.939451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3573d073-39b6-476d-bb92-6410b8fd93d8 · outbound

This paper cites On the feasibility of simple transformer for dynamic graph modeling.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics On the feasibility of simple transformer for dynamic graph modeling

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.544351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d809ef00-1db6-4e0d-8345-9d3e613efada · outbound

This paper cites Inductive rep- resentation learning on temporal graphs.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Inductive rep- resentation learning on temporal graphs

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.527844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e54dbc5-87b8-4892-9147-68fb39d061f4 · outbound

This paper cites an unresolved cited work.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-09T10:31:16.493848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f60f76e6-a1d9-4dff-a594-55a2fb7ef9bd · outbound

This paper cites This power-law behavior indicates that while a few hub nodes have a high degree of connections, the majority of nodes possess significantly fewer connections.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics This power-law behavior indicates that while a few hub nodes have a high degree of connections, the majority of nodes possess significantly fewer connections

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.476152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9f5368f1-ee61-4d12-9cb4-94f7710fc34c · outbound

This paper cites The Patent dataset is quite special that all citations of one patent are labeled with the same timestamp, specifically the publication time of the patent.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics The Patent dataset is quite special that all citations of one patent are labeled with the same timestamp, specifically the publication time of the patent

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.460248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9523a024-4049-4f49-b8bc-3daa4bb771d7 · outbound

This paper cites It includes a novel projection operation that predicts future representation trajectories of both users and items, allowing the model to anticipate future behaviors.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics It includes a novel projection operation that predicts future representation trajectories of both users and items, allowing the model to anticipate future behaviors

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.427592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44207d35-e239-41f3-924c-9415f1db7c86 · outbound

This paper cites It memorizes observed interactions and uses various strategies to update its memory.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics It memorizes observed interactions and uses various strategies to update its memory

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.408417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 74eb6977-f777-4f1d-ac54-2614d860ef61 · outbound

This paper cites Table 6 demonstrates the performance of existing temporal GNNs on the GoogleLocal dataset when incorporating node features or edge features.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Table 6 demonstrates the performance of existing temporal GNNs on the GoogleLocal dataset when incorporating node features or edge features

Reference 172

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.443801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 704c1709-eaf6-4681-bf02-cff9aa3bee55 · outbound

This paper cites The nber patent citation data file: Lessons, insights and methodological tools,.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics The nber patent citation data file: Lessons, insights and methodological tools,

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.644878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e1fc30b0-3e4e-4ca5-9c75-82c6c64185da · outbound

This paper cites URL https://doi.org/10.3115/v1/d14-1179.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics URL https://doi.org/10.3115/v1/d14-1179

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.868455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d11e4d59-783d-422a-8d47-4c316015a584 · outbound

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

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Towards better dynamic graph learning: New architecture and unified library

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.510316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f45faf37-9267-4a3c-ae4a-2dcf9c79ec79 · outbound

This paper cites Benchtemp: A general benchmark for evaluating temporal graph neural networks.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Benchtemp: A general benchmark for evaluating temporal graph neural networks

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.622820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5dbafa5c-a9fc-473a-8052-3756f1f160bd · outbound

This paper cites URL https://doi.org/10.1145/ 3292500.3330895.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics URL https://doi.org/10.1145/ 3292500.3330895

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.894280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f77f2a8-695d-4464-8240-8206f9e0f56f · outbound

This paper cites Network science.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Network science

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.661092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05521a09-19e2-4d18-b175-32472d6f68ef · outbound

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

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics Predicting dynamic embedding trajectory in temporal interaction networks

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:31:16.607656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 958176d6-e40c-42a4-ae2c-ffe181721fef · outbound

This paper cites URL https://doi.org/10.18653/v1/2022.acl-long.426.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics URL https://doi.org/10.18653/v1/2022.acl-long.426

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.899315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c192be50-e31f-439a-b2d0-136d99c9e2f2 · outbound

This paper cites A survey of dynamic graph neural networks.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics A survey of dynamic graph neural networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.959317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e33c975c-a802-4554-ba0e-686df6e3561c · outbound

This paper cites ISBN 9798400703713.

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics ISBN 9798400703713

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T10:31:15.863440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b31d4381-f3fb-4b6d-ba94-876a3fb76bdf · inbound

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer cites this paper.

TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:10:34.172591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45e3107a-8676-4369-9b76-affca63deecf · inbound

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs cites this paper.

T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:46:34.354641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.