Paper Citation Record · LEDGER

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures

As of 12 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2412.21046.

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

pith.paper-citation-record.v1
2412.21046 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:21:33.930030Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

23 of 23 outbound references displayed

verified exact4
verified fuzzy6
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0cfc6eb7-fc44-43c2-9ac4-1204d4fca28f · outbound

This paper cites DyG2Vec: Efficient Representation Learning for Dynamic Graphs.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures DyG2Vec: Efficient Representation Learning for Dynamic Graphs

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:21:34.286230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 84c03715-c8c8-46de-8428-1a01635350ee · outbound

This paper cites Optimal Kronecker - Sum Approximation of Real Time Recurrent Learning.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Optimal Kronecker - Sum Approximation of Real Time Recurrent Learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:34.379536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 64b62c0a-8994-453c-af42-994754d2fada · outbound

This paper cites Neural Ordinary Differential Equations.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Neural Ordinary Differential Equations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.834448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc630382-85f3-4cd2-adf4-b2c92616900a · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.839396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34fe1015-43b0-460b-a691-3f71360d94c7 · outbound

This paper cites Deep Coevolutionary Network: Embedding User and Item Features for Recommendation.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:21:34.246407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2e84e92f-3a82-4075-bf39-1a8b0e8d064d · outbound

This paper cites Long short-term memory.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Long short-term memory

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.848855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c52038ec-5990-4332-951c-6b68500a9953 · outbound

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

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.854214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae784294-8b91-41fe-a1ad-843ae1d40b2e · outbound

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

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:34.357281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8e066b33-a6f9-48e5-9b6d-98337f6a9217 · outbound

This paper cites Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.862822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6e215f0-91ae-439f-8ede-811306a60521 · outbound

This paper cites Decoupled Weight Decay Regularization.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Decoupled Weight Decay Regularization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.867436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6c67f0-18de-4dc4-b9ce-4e31101b08df · outbound

This paper cites Approximating Real-Time Recurrent Learning with Random Kronecker Factors.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Approximating Real-Time Recurrent Learning with Random Kronecker Factors

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:21:34.128403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 48594983-5628-495d-973f-2e64e62a9dc8 · outbound

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

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.877222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a55ed3ce-402e-4730-9a18-0e7472874ba0 · outbound

This paper cites Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:34.345393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e97f591a-28c3-46f9-a431-9c2524449e0a · outbound

This paper cites Unbiased Online Recurrent Optimization.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Unbiased Online Recurrent Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.887021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe547084-2c12-476f-8153-3b652a3628e4 · outbound

This paper cites Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:21:34.085962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a8665e1c-e952-45c7-b099-8f9e77697521 · outbound

This paper cites Dyrep: Learning representations over dynamic graphs.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Dyrep: Learning representations over dynamic graphs

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:34.332531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ee860b8-66a1-4ce8-93eb-61ec3b9ca713 · outbound

This paper cites Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.900634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0346e1ef-1f3d-425e-8322-f412d1464057 · outbound

This paper cites Coevolutionary Latent Feature Processes for Continuous - Time User - Item Interactions.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Coevolutionary Latent Feature Processes for Continuous - Time User - Item Interactions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:34.319314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9bdae44e-b7a6-4a43-96ce-4cf1e3d117a1 · outbound

This paper cites Inductive Representation Learning on Temporal Graphs.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Inductive Representation Learning on Temporal Graphs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.910554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3f001bb-34b6-4d5b-93f7-30c1c3ddd762 · outbound

This paper cites Towards Better Dynamic Graph Learning: New Architecture and Unified Library.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Towards Better Dynamic Graph Learning: New Architecture and Unified Library

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.915472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 214842a4-a56d-461d-94e2-5ea3e6d0a382 · outbound

This paper cites Disttgl: Distributed memory-based temporal graph neural network training.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Disttgl: Distributed memory-based temporal graph neural network training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.920071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e1d44e3-dab3-4c79-b2bd-437bb9ca91c9 · outbound

This paper cites Online learning of long-range dependencies.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures Online learning of long-range dependencies

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:21:34.306409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c24fd205-7b93-4d90-bb47-c4a786e79cec · outbound

This paper cites write newline.

Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures write newline

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T23:21:33.930030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.