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Paper Citation Record · LEDGER

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks

As of 18 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:1908.05365.

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

pith.paper-citation-record.v1
1908.05365 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:23:02.876298Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved8
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c4827670-181b-4bee-94e1-47e4ce8dd0ab · outbound

This paper cites B.3 Network Structure In order to assign each of the∼160M yellow taxi rides to a pair of vertices in our graph, we must map their pickup and drop-off locations to a census block.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks B.3 Network Structure In order to assign each of the∼160M yellow taxi rides to a pair of vertices in our graph, we must map their pickup and drop-off locations to a census block

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.468766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.756577Z digest=sha256:73ce01d0a98f8a3468da15712bfd559368abd5ab9408fe09598a7358b268ba24

Observation f9e5ee98-8a02-4f03-8313-7aedc33e5c05 · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:23:03.363439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.805837Z digest=sha256:d9fe1ce6a75c71d7bc76aebaf40b74602c42ac56a8783efebff5b4e04eba737b

Observation 8556a753-62da-4456-bd64-d6385a9fc3b2 · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 3

Resolution
parse uncertain
raw_fallback, observed 2026-08-14T13:23:03.514088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.735686Z digest=sha256:6feaa28e90c414102af3247881f0508411ec50f49643d6f2fdb587b6ec9f3c87

Observation 9d1fbd17-cdaf-4178-b0d1-e4374c7cffc7 · outbound

This paper cites Hermsen, Peter Bloem, F abian Jansen & W olf B.W.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Hermsen, Peter Bloem, F abian Jansen & W olf B.W

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.493011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.745582Z digest=sha256:34a65417fefcc3ffb06484b1c297e8a4cd662956c884beaf3fd7cfd3652b5d63

Observation afa64609-8a47-4b0c-8376-d7d5696fdc18 · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:23:03.393302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.799135Z digest=sha256:fa2bed6e046393866b89c8afaca1f29e2560760c086d60b6a9e8110b2a7a6648

Observation 01e092b8-88a4-4daa-b5c0-2652911ff059 · outbound

This paper cites Floris A.W.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Floris A.W

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.426198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.785279Z digest=sha256:b425c24ab33c54af28735c09ba3c4c71421fa3933bee4f84af633b90d43b27f0

Observation 8b1d048d-4549-4783-b5ea-ff81347212b9 · outbound

This paper cites Types 1 and 2 receive additional small, randomized offsets on their time attributes in order to introduce a degree of noise.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Types 1 and 2 receive additional small, randomized offsets on their time attributes in order to introduce a degree of noise

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.330368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.812884Z digest=sha256:94b2b72e606fa896c8ccf7177e6ab15697227251a23dc20863e75c4ee07d3f98

Observation 195f24d2-aeda-445d-98c2-50b7215ed944 · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:23:03.300482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.821595Z digest=sha256:f71a1769150beca4d5312bf85c5c116d394c340bf0f638f72e6db82a38c5defb

Observation ae52b278-210b-45b2-9433-ba042ac553ff · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:23:03.266358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.828006Z digest=sha256:876ee88334215ce80c418c04077072af778dca7d246418466d234d588be90ba2

Observation 50be4929-87c0-4222-bbb5-0bb756919e50 · outbound

This paper cites In case vi∈ N andvj∈ F we introduce fraud type B, having similar but opposite effects:.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks In case vi∈ N andvj∈ F we introduce fraud type B, having similar but opposite effects:

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.243062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.839599Z digest=sha256:b9db2b96d946cae98643da59cceba59cbeb4435517918cadad3d5b4384c293fd

Observation bba76970-7a43-4944-9b47-0b43c02364b7 · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:23:03.207802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.848777Z digest=sha256:05099c072b0baba639656026244b19ffbbf7ebdf6b37e61ee39b1132a0b3f659

Observation 994953ac-9afc-422a-b451-24a43cc859b4 · outbound

This paper cites an unresolved cited work.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-14T13:23:03.172644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.856781Z digest=sha256:309367928cda1c4d0c903d1be93fc0c21d9a300cdc282701715de190d54477ce

Observation ff16a6f9-3424-49d1-b5b5-f17da2ce3f36 · outbound

This paper cites 11All of these modifications take place with a per- transaction probability of 1/3.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks 11All of these modifications take place with a per- transaction probability of 1/3

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.138902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.867730Z digest=sha256:7186d99d7dd43eb7fd4de374b489c106e30f3a9451fcf77cff3aed81f436ac0a

Observation 3bf0e3d8-3450-4cde-a658-fad725f7e9ca · outbound

This paper cites Values for|E| also differ slightly because of the removal of generated self-connections.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Values for|E| also differ slightly because of the removal of generated self-connections

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:23:03.093371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-14T13:23:02.876298Z digest=sha256:63a1972a17606a4f13bb98bdb74ce321d21ecf716101f229b77efdd0e833a195

Observation 2b8c21f7-ece0-4f53-89ac-69b1395c4a3b · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Relational inductive biases, deep learning, and graph networks

Reference 2002

Resolution
unresolved
no resolver link, observed 2026-08-14T13:23:02.712761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:23:02.712761Z digest=sha256:5f1037e16778beccdf9af78c6cdb926070c7ce874358a23ea2a70a900478be73

Observation 605e780c-8573-4236-95c1-4e1460bca8c9 · outbound

This paper cites Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.

End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional Networks Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-14T13:23:02.722286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:23:02.722286Z digest=sha256:7f7f104e45ed9190df2e4743344eb7377eae03edc5290a5b972f103df5b1bec6

Pith citing papers

No inbound Pith citation observations are available.