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Paper Citation Record · LEDGER

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2506.19282.

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

pith.paper-citation-record.v1
2506.19282 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:04.703179Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

47 of 47 outbound references displayed

  • verified exact3
  • verified fuzzy6
  • unresolved36
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b58bf228-8dfd-4cd4-82d6-0dea39229bc3 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:12:09.984318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:01.769421Z digest=sha256:75bcbaf9a4f4095b55646ea7c31d1175b7db26612325b4e225b4954a1fa7a927

Observation 0bf46ab9-4c40-45f0-af31-eda8c2e20c90 · outbound

This paper cites Arghal, E.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Arghal, E

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:12:09.828953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:01.903983Z digest=sha256:cac52a128d0c6cf7338590c448acd7b8edbc9297283a880e686402d000560679

Observation 368e8f84-bdcd-4a54-99f6-5d652a7f1283 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 3

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raw_fallback, observed 2026-08-06T23:12:09.703321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:01.944533Z digest=sha256:9175de73e6d4398a1de5955c9254b558724f07719eb0c3bcc3a74654fa861316

Observation 143bcd90-730b-492a-a36a-0cb688db44f9 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:12:09.550493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.029043Z digest=sha256:d5e4d86751fdfa6261ea58fa71ff0db7cd13adae538321f193ed1a62adf42556

Observation 8a2bc3e0-0e9e-4134-bfa9-f57a404f68da · outbound

This paper cites A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T23:12:02.087994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:02.087994Z digest=sha256:ccfca0cbd09206e44d9cdde17c32040d51492459f95aa3e8cf975edb41c9a884

Observation df0748c1-b7d5-424b-b2ff-2d8e8dab72eb · outbound

This paper cites Upper and lower bounds for the Lipschitz constant of random neural networks.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Upper and lower bounds for the Lipschitz constant of random neural networks

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:12:05.296721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.172706Z digest=sha256:de32d1294841cbf54501cca1fe28f5a90c68ff18978dcd760edd144357826d94

Observation 007c6f6b-1bd3-491f-942d-9827ee460045 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 7

Resolution
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raw_fallback, observed 2026-08-06T23:12:09.385683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.232688Z digest=sha256:d8b591699d9497376ec0b6886d06c2944f73f0e2253c180b0320faaacd97ccce

Observation 22ed8b43-4317-42da-b4fb-8439d6ae6562 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 8

Resolution
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raw_fallback, observed 2026-08-06T23:12:09.271850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.268126Z digest=sha256:6c1c5387cb9bbc1553ab90e98217b0ad5f5d9f24b34b2e7a4de3f1e8f85b8365

Observation d7b47829-7e56-4606-aa91-3b107f4273cc · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 9

Resolution
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no resolver link, observed 2026-08-06T23:12:02.331293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:02.331293Z digest=sha256:31677fccfedc34554e6dbd7d6d4d64263250a74fb0891e780522ad97a6ba4b4d

Observation 53beb072-f074-4d7e-a588-c1ce1704eeab · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 10

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no resolver link, observed 2026-08-06T23:12:02.373511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:02.373511Z digest=sha256:6f0e0238f507ed631e69b33defd2a58afbcf234c6bbf14b570845eabb86ed749

Observation b10df4ec-0910-4ec8-9747-89a4496c795a · outbound

This paper cites Hamilton, Rex Ying, and Jure Leskovec.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Hamilton, Rex Ying, and Jure Leskovec

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:12:09.118895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.420518Z digest=sha256:fd4cb74262b526a3604db58f1454864005451b9ddf294282f9bf938e9e778c29

Observation ce1288f4-fab0-4783-b97f-68e92f98f70e · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 12

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no resolver link, observed 2026-08-06T23:12:02.462283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:02.462283Z digest=sha256:82999b6905b0ce6ad990e650affaa3e1c912e192c1cf46213c9beac5a2d2aa7e

Observation a3e3c47e-0567-4564-8142-5d6269c55ea6 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 13

Resolution
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raw_fallback, observed 2026-08-06T23:12:08.827303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.574236Z digest=sha256:d05f597cf1c829d2e8877633ee77b355a47ee901f6f4726bd85c81f7e146b891

Observation fabcea6f-0cd3-45bd-ae61-b91aaa5009b0 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 14

Resolution
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raw_fallback, observed 2026-08-06T23:12:08.686181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.652209Z digest=sha256:7261984fcdf16ec9b6a4050debf3f2531f732c4cd6524cd2b80afc621f149ea0

Observation 1d932dcc-daa1-4fca-b007-3542984ab0a6 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Semi-Supervised Classification with Graph Convolutional Networks

Reference 15

Resolution
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no resolver link, observed 2026-08-06T23:12:02.731887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:02.731887Z digest=sha256:fe68ca6304ce22011e2cfbc6ca18b0d86eb533f624e2d668375d0c8f2248853d

Observation d7d513dc-4dad-4bb0-9a4a-84f045e97628 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 16

Resolution
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raw_fallback, observed 2026-08-06T23:12:08.570195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.769254Z digest=sha256:a76984d3eb6314c0406aff4e9a898a96df7174ec32a80fc87247a22a0a2af1d6

Observation 91fa7373-fb5f-4004-ada1-3b47808ddc66 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 17

Resolution
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raw_fallback, observed 2026-08-06T23:12:08.429771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.840213Z digest=sha256:61a568bf4c67d2f57d5e34fb3ad41e24cd75892a6f327c0fd9a7052ce457ccef

Observation d8e6747f-16d4-4869-94ce-846b73c135cb · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 18

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no resolver link, observed 2026-08-06T23:12:02.894199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:02.894199Z digest=sha256:3940774508bfbf6600d1f4fb9e86aaea67d32eb111c4bc62b0637802b39761df

Observation 4cac62a6-e8da-40aa-b094-356cb71359ca · outbound

This paper cites Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:12:05.170054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.954516Z digest=sha256:1ed045f1eba4496d4dd3facc3420ee47edda8040b44bf64b942d13d53bb96938

Observation 90689471-5068-457b-98fb-5c63f3ebe8ba · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 20

Resolution
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raw_fallback, observed 2026-08-06T23:12:08.280180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.019024Z digest=sha256:c1d812f63e6f2bce83d0eced32d35482902fbe5849b43c5968181aa184178e0e

Observation 5f3fafe1-291f-428b-8dda-bfa31875abda · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 21

Resolution
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no resolver link, observed 2026-08-06T23:12:03.066531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.066531Z digest=sha256:0bcd3f44d6c748d67890db3c17f04940ab905881f2df435c7ecf7aac3846a919

Observation 08156981-c23e-4cab-9799-f5e1eece2a40 · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Geom-GCN: Geometric Graph Convolutional Networks

Reference 22

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no resolver link, observed 2026-08-06T23:12:03.113234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.113234Z digest=sha256:3b92daf5d4293a385669190acfd1073a18a3d6cd947f5b736c1153d97b8d925d

Observation 4f476f45-a5df-4ac4-8e56-e41bd833b06a · outbound

This paper cites Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Understanding Optimization of Deep Learning via Jacobian Matrix and Lipschitz Constant

Reference 23

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verified exact
local_arxiv, observed 2026-08-06T23:12:04.988291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.201720Z digest=sha256:ab8907727cad4d84bb8600b13e714b60ef775f66d4d3c4ae2a3c2e051fa9acb3

Observation ccfb032e-6b78-4206-ae7f-0970bb9b1ebe · outbound

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

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 24

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no resolver link, observed 2026-08-06T23:12:03.253416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.253416Z digest=sha256:3dae64a8005cbed1114d861edf3b500552d3a85d464ed12e13b3930209dd3501

Observation 6419544e-9410-427e-a956-1b37cee4e728 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 25

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raw_fallback, observed 2026-08-06T23:12:08.158063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.317954Z digest=sha256:4089c854b489c9eef8a7ad2b84fece497253610f2151ad64a9bc989547f87c87

Observation 161b8baa-cb6f-4253-8677-8fbc8b0208df · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 26

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raw_fallback, observed 2026-08-06T23:12:08.021418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.424586Z digest=sha256:0e74d7edb31f186b5da76ff1322bcbc92e334f423cd30bac21ce4417a21dee6b

Observation 591511be-d439-43b3-a75d-da6d750b8d88 · outbound

This paper cites PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks

Reference 27

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no resolver link, observed 2026-08-06T23:12:03.489658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.489658Z digest=sha256:6438b9167717a7399f5db342d0d00fb6626ba67e6f746bce4ee72a63dadaa0c7

Observation 40b30211-68bb-4dc1-88f3-431c4501c4d7 · outbound

This paper cites Szegedy, W.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Szegedy, W

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:12:07.910441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.585992Z digest=sha256:62e3c5d1430bd6014f9bb9606770e62aaffa28f04641fc3f0d12ff33f703fe13

Observation 4d284501-88ea-463c-8469-3a06ca97ee95 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 29

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raw_fallback, observed 2026-08-06T23:12:07.710313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.623600Z digest=sha256:7a3dc88fc901192a1bf25d661e59fb2128aec5e90c9a27de45bede91a9d90b79

Observation 332b55c3-25a1-4657-8b93-a40aaa1ed182 · outbound

This paper cites Graph Attention Networks.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Graph Attention Networks

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.704804Z digest=sha256:26a23b4a854393cfa508a598d93d41b4399b6439167cada54f5d0ba6f06dda4f

Observation d85c2999-205d-4041-9999-d9a04ee30308 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 31

Resolution
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raw_fallback, observed 2026-08-06T23:12:07.518484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.782486Z digest=sha256:36da45524baed69de2e33a5323422aceef87af8252c38d16c9a92f61e84873b9

Observation 1564a082-e6c6-4517-bebb-3575418e22aa · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 32

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raw_fallback, observed 2026-08-06T23:12:07.292488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.843793Z digest=sha256:645d436e21e6dea88374b3cb50502a647e6b0a22148bb3a3aed10a548be4ce83

Observation ae663a5d-650a-4eb4-bb80-9757dffb7890 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 33

Resolution
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raw_fallback, observed 2026-08-06T23:12:07.060441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:03.925662Z digest=sha256:d3f997c5ad5cb83d5608b884542746e7407559f0593c5fa6d18c369b14ee2489

Observation 58891532-5e85-4054-a810-85761be6eb2d · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 34

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no resolver link, observed 2026-08-06T23:12:03.976862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:03.976862Z digest=sha256:2e2f39d68b251dd003ed8a99ea2528b9fdbb51d335ba2c58a292c1ea91fc91fc

Observation 5855fcc8-731b-4e88-b3c2-db2edbcc4c08 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 35

Resolution
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no resolver link, observed 2026-08-06T23:12:04.035417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:04.035417Z digest=sha256:70aa3c763ccd62cd940d021bb0387744027d1f8ae8c17c352aa413578f8c0105

Observation 1a13bc4d-3022-4932-9950-0b3f7ea4f1b1 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:12:06.775343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.151682Z digest=sha256:c7688f8965c922b95469fb775c38ca196bf67cbbab29705ec2698e6feab8c964

Observation e5784359-d934-4e7f-a347-3019b5d27aca · outbound

This paper cites Hamil- ton, and Jure Leskovec.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Hamil- ton, and Jure Leskovec

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-06T23:12:06.524334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.233418Z digest=sha256:30953b0fe8011e97b5cb6d1318664e9323a199651f823c2b6857d2e1bf8d61ba

Observation 0059f42a-9cdf-4a54-84e3-06e3aca0b649 · outbound

This paper cites Inductive Representation Learning on Temporal Graphs.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Inductive Representation Learning on Temporal Graphs

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T23:12:04.111354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:04.111354Z digest=sha256:97c0f3c42e56c69aa91b901143a5f77ef2c32e86b22d1f6b5ca0b0ada35871d8

Observation 9e309b1e-4a22-4e10-a5b9-f2c14c376a8f · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:12:06.016562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.379516Z digest=sha256:e7a7fd2ce6d215f96790790a680bcff3c79c985ad8983a8d3295daf956fe5737

Observation 6823225b-f6b5-4cd2-b4ef-fe36ab1ea90f · outbound

This paper cites TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T23:12:04.524177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:04.524177Z digest=sha256:3d1262115b2fb854a1345d425cdf6fd86cd0a24cdd22b26a7077509ef52bc7b6

Observation 09687911-6a94-4a9c-a990-745bc56cdf5c · outbound

This paper cites Mueller, R.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Mueller, R

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:12:06.268427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.312617Z digest=sha256:098920e8c4e561b62edc37d35ac6fefad4704073ebb67440a7cb4e30dbd54fca

Observation 311939f6-b004-4e8d-abf9-e3d25af0d122 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:12:05.739361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.665466Z digest=sha256:515917f2643bac7e960a67d8576b04a1779dda1c6b6325583a9359f6a3d96bd9

Observation 96719a7e-a689-44be-b5df-e8c4cdfdc030 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:12:05.871938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.467841Z digest=sha256:9363c932866422fb1f7db907c95f583a2bcc745c7f07151ab326d1ac07418371

Observation 7d394e6e-15e5-47fa-825a-68771aa3099e · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T23:12:04.607710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:12:04.607710Z digest=sha256:3060f47573a35ce45df1c84fb1e9f6634c83dee93956d0c9a6f7509a49bc3cdf

Observation 529f0795-0179-4031-97e5-050813803db2 · outbound

This paper cites an unresolved cited work.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams Unresolved cited work

Reference 47

Resolution
malformed identifier
raw_fallback, observed 2026-08-06T23:12:05.600728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:04.703179Z digest=sha256:882ec43e8b5dae63889c263386225a45eb3c710ecac4c99fb2ed5c73bec9d6d3

Observation 3f54cb03-1b82-4c11-9620-bf04c8d8f626 · outbound

This paper cites On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory

Reference 2020

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T23:12:05.452275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:01.858488Z digest=sha256:4ff1f34bd8591f6dc3e40bd90a7aaf5c2c6132a87c84360c294bb6667af0dece

Observation ce95b0dd-7fe4-4514-af8c-8bc45c22da4e · outbound

This paper cites In Neural Information Processing Systems.

A Batch-Insensitive Dynamic GNN Approach to Address Temporal Discontinuity in Graph Streams In Neural Information Processing Systems

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:12:08.982282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:12:02.528938Z digest=sha256:d8e02b0f0f7b99074cf9ff6ba5fbe1bd364d9ade6fb67a4ff1a0d2803418c1ad

Pith citing papers

No inbound Pith citation observations are available.