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Paper Citation Record · LEDGER

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks

As of 19 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2508.18052.

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

pith.paper-citation-record.v1
2508.18052 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:07:21.637122Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

11 of 11 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65babbfa-b4c2-4654-8113-2a5ae34fe7db · outbound

This paper cites In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=lxHgXYN4bwl.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=lxHgXYN4bwl

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.833071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.584308Z digest=sha256:db290facc48dd73684c07017805e8a17f7f1e7302ddcc6f244bb47e49554d7a9

Observation 33c58a4f-7c57-4740-8602-c4fe99581214 · outbound

This paper cites In: Topological, Algebraic and Geometric Learning Workshops 2022.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks In: Topological, Algebraic and Geometric Learning Workshops 2022

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.817397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.590487Z digest=sha256:e605bf494f601c0af660cf3c0008518fbba5282c013be655eea5c8c35007e871

Observation cfd87126-773b-4565-8f7e-5fa402283923 · outbound

This paper cites Neural Networks173, 106213 (2024).

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks Neural Networks173, 106213 (2024)

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.801159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.596064Z digest=sha256:4b934db4d849ed0f16b9d2a97df33cb4dcdfc1d24ba4b92765e4984573a3da63

Observation ed53e976-d246-48cd-b970-047cc67d3adf · outbound

This paper cites On the approximation capability of GNNs in node classification/regression tasks.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks On the approximation capability of GNNs in node classification/regression tasks

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T17:07:21.601278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:07:21.601278Z digest=sha256:7e999372b8b3c6d5829b1667aa71bc2b7bf0ba22d6b50f89bd97d645f668cdd3

Observation 68bf6ea4-50d0-4fdc-9928-4ebfca3fcc9b · outbound

This paper cites In: 2015 30th annual ACM/IEEE symposium on logic in computer science.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks In: 2015 30th annual ACM/IEEE symposium on logic in computer science

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.784611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.606956Z digest=sha256:570890685965cc75d5a4447ca97a742c15e5f3ce28c66eca03b2989587d45c26

Observation 66787ea4-7686-49aa-ba4b-71e1ecdebac6 · outbound

This paper cites Physical Chemistry Chemical Physics 22(45), 26478–26486 (2020).

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks Physical Chemistry Chemical Physics 22(45), 26478–26486 (2020)

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.769167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.612259Z digest=sha256:0aa82bb2d5b0914632b0fe4dac6753733751a4ed0de231098544a3cb1e65bfb1

Observation 9c77225e-cdfe-4218-80d3-c67c1b9348a1 · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T17:07:21.617750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:07:21.617750Z digest=sha256:06f4ffc78bd74ad19f0dfcf96cfcddf59a7bfef300a759f472324f56a1fc88c8

Observation 237ea73f-92e7-4300-91dd-bdde8fd0cea7 · outbound

This paper cites IEEE Transactions on Neural Networks 20(1), 81–102 (2008).

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks IEEE Transactions on Neural Networks 20(1), 81–102 (2008)

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.753763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.622734Z digest=sha256:2ff5578edf799971898e54850b0ef42036ebc5061ffa4f2ef808c5e71f5ca44e

Observation af768bfb-2348-4c0d-a7cc-d10ab53ea888 · outbound

This paper cites iEEE Access9, 79143– 79168 (2021).

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks iEEE Access9, 79143– 79168 (2021)

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.737342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.627575Z digest=sha256:2233b32065ebef07d92042554c0d9f833c9529ed5efa10b60c4993def0fa5ce1

Observation 80462aaf-ee66-428f-a5e1-0b349311d95c · outbound

This paper cites In: 7th International Conference on Learning Representa- tions, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks In: 7th International Conference on Learning Representa- tions, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:07:21.721685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T17:07:21.632379Z digest=sha256:475e3686dab8c9aff3ef5d0daa1fc724d4c33500b190e9c646750d1550bb2e07

Observation 62123336-2a55-4078-b82c-0f29f62e3cd9 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks How Powerful are Graph Neural Networks?

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T17:07:21.637122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:07:21.637122Z digest=sha256:f09c938c67084016abe618ffc8602c7f93397a206a0df1d73f904596c4d11314

Pith citing papers

No inbound Pith citation observations are available.