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Paper Citation Record · LEDGER

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

As of 7 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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:01.769421Z digest=sha256:56795952a19a2e0665054412bd089adc36872ec28fb9101545979173cefb3a59

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:01.903983Z digest=sha256:fa1fdd0b5b2b6863a477dbc90dd8e36a0c2785c46ac2b1047e36f9aade133f4e

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:01.944533Z digest=sha256:6b86a00d40ee4b7dce281ea0bd7b0c0bec16bfe6b8f011080eb13e818db0aab1

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.029043Z digest=sha256:85c92e00fe264afc441f787db9e8883b83924b63db6e297fb125d01f0e841b8c

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
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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=sha256:6c4c7454f2ce50f478198a133988c517bb99c595ffea8cab9dbb7e8e15aaadfe

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.172706Z digest=sha256:fa5b444dc468a465a261e9e367cd1c8043221e3883806df436d176fa1f38571c

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.232688Z digest=sha256:71c3224f8e1bd0850ca888b23eb2cb32212250dc2ddb0d8617028f492f414a80

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.268126Z digest=sha256:f992a2165e6f747ad352af0309128c269b8c7dd64f50f249d6a675aef024fe75

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=sha256:28b086993d7cbcb6d520ce05f8ccbe71a8c2ef0e6021db766d3ff21b7cacc27e

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=sha256:68a42e4827f53ebc86e36bc0c1272f117fe582f9ef641d2c251cec27e8930b3a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.420518Z digest=sha256:88e570ec6e5b4cc05fb89136d1eef0bd7cdafa69b6a113585b23e5118b2f0510

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

Resolution
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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:e59cdaae4f5111549c0e6547fd0941d527e180d8948a8769cba8c63879efb425

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.574236Z digest=sha256:42e5c6525fceb7ab78b12497acb3986aacb820e0deabda1a13e3f4e1d25d7816

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.652209Z digest=sha256:5181a54682ed2c10e4e321e8714cbeb98b1a9f8c476bcd9efa68205ff2008f85

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

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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:ccf9cfe7953208858d42e076fd5ec39b60cdddc838431e756dbc0bed6957ea9c

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.769254Z digest=sha256:bef9445c59ef45f3b433cf7883ea785f821cdd71417d7c8ba5011b8d34e57ba0

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.840213Z digest=sha256:ae8787c9474cf6b2cdb8e0c53f59fa6473b6d564bcfea30f510d6d8460d0bda4

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=sha256:3842773d5a5f004bf2a4b047d20f6a742b2f6cee174298fe567d17db1f385e9a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.954516Z digest=sha256:e230350268e327af15c94eccfce2192f72f7f90bd7ce2f08999f6ad2cc411134

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.019024Z digest=sha256:54d1369c2424c9057d53edb4e73ae53c1ace862aab4ee2d17b1b454822b7e633

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=sha256:39c62eebea892138811f388fe24b53fb3b97653963a6da43ece9ad5a95deecdf

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=sha256:560ca880b761e2bc9f0cbe421a45d36898e08825420e0cac02226d1ca8442718

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.201720Z digest=sha256:b91c537a1669b6472b2e3f80e1edb008fa52d31656fdaf697c01de00dd5e5936

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=sha256:98c9df4c1e59f09bffc503454007e36808c5b546be4664a0e7ffb706678ed271

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.317954Z digest=sha256:6e238bc2ba2ffd7e6063b8c4c08997cc1a64517edae7a76be7637ecb024c52e5

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 2026-08-07T06:34:17.273281+00:00.

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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=sha256:3914bd053d1390bb69be52581bbb07efea678fd2c2fbcf18e7abb2a6ca1b0380

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.585992Z digest=sha256:a8ce04214db9bfb4200967165fc9e3d7d66b39443502200a8f7b7e826031d7bc

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.623600Z digest=sha256:9f4bff09a3611a6615d7980721e3e3b9e31892cb54f7bdca07f48083ebb7642a

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:a38a9344ec8fcda343351e3d5a9ee7ab821f9cbfc1d4ef606bbd67af021dcef8

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.782486Z digest=sha256:c7176966ced0a220f86889e2adcbbf6064e551eaeb57229d1210a8012b2d75cb

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.843793Z digest=sha256:d5de20e42de907a3dc57ae1ed2b2c59104a93861c50612b10d24e7689b2d4c03

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:03.925662Z digest=sha256:a0a58943d9ed7d8bf0a4c0e506961a5af3d99098aeebbdbae519075270a1dc79

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=sha256:01ebd08d063383106e5e079e45998a5f752f7351681fa26b439f040a8d51dfbb

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=sha256:3526e41618abe8cf9c31193452d93c05ffdef17d56f8b9089818054dfc2e2bdf

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

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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.151682Z digest=sha256:3ebb6763385ef41cb9ba98351ce643140609682dc3bf8803fb6dbd480827d6a9

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.233418Z digest=sha256:749db4873a594cf1907ebdaa862cb2eddd66de0575bc522f28bf62c6ea3f482a

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=sha256:870dca545280e3fb7d1fc1718daa2be9cdee1823aa606c73510b3d7149e839c5

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.379516Z digest=sha256:94bfbb44c1f90c04c364837a277e28e3cae97ae631a1df209dfd8c940fd9f522

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=sha256:72bb7afce1707f09bfcb8af61fac5d17b19dcb155aa7f582a0ab0a4fccbc83b5

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.312617Z digest=sha256:3d9419a3f71025a280711dfdb368a2168cc2e58c14625286325db776cc3688b2

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.665466Z digest=sha256:99b0bf98289ad9ef260ee2af19566b04bc5e5dac0c0caffcc8c770f5a534fee5

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.467841Z digest=sha256:69479248b91b5649e61750a6c6fd4c08ab3465293dc60998e2be397ad04ab29f

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:aa373fad67aeab2b510728d6d14aa857438a4670d0b881995f23dc448e04a4aa

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:04.703179Z digest=sha256:e8e8a41057e6a824be345284e91221b21c4f1d979b6fddb19098953afcae6c5a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:01.858488Z digest=sha256:b105f59b39ef15cbbe9908994f66cf7ce9b5fbe0134b7b7ff20f195daa92a4d6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:02.528938Z digest=sha256:1b906e68dec8c25daa346a22cc3e256c85ba17ba8c86fabaa35f16e99ce7870f

Pith citing papers

No inbound Pith citation observations are available.