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Paper Citation Record · LEDGER

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs

As of 11 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.27861.

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

pith.paper-citation-record.v1
2607.27861 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T00:16:04.591477Z

measured 21 of 21 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

21 of 21 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cf969f8-18f7-40ea-9c49-8163b6b7550a · outbound

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

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 1

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Observation 211a9911-ebec-48df-9a41-10582f502772 · outbound

This paper cites Towards better evaluation for dynamic link prediction,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Towards better evaluation for dynamic link prediction,

Reference 2

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Observation 0009393d-4769-45f7-9d14-c86ac0f47eca · outbound

This paper cites Temporal graph benchmark for machine learning on temporal graphs,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Temporal graph benchmark for machine learning on temporal graphs,

Reference 3

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Observation d6a6e014-f013-493c-9c28-c4eb2460ac8f · outbound

This paper cites New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs New Perspectives on the Evaluation of Link Prediction Algorithms for Dynamic Graphs

Reference 4

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Observation b11a4d1c-7168-4877-ab65-6d79482c1fd4 · outbound

This paper cites TGB 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs TGB 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs,

Reference 5

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Observation c4d8f79a-1b63-49b0-ba64-bf7e8e50b7f4 · outbound

This paper cites On sampled metrics for item recom- mendation,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs On sampled metrics for item recom- mendation,

Reference 6

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Observation d09dc8bc-ea1d-4ca7-8fa8-010ce10b2e67 · outbound

This paper cites A case study on sampling strate- gies for evaluating neural sequential item recommendation models,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs A case study on sampling strate- gies for evaluating neural sequential item recommendation models,

Reference 7

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Observation 026b0efa-30b7-4000-bf7f-17eb413adfe8 · outbound

This paper cites Towards better dynamic graph learning: New architecture and unified library,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Towards better dynamic graph learning: New architecture and unified library,

Reference 8

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Observation 271d6e64-200d-47df-b562-fe6ddc1851a9 · outbound

This paper cites Efficient neural com- mon neighbor for temporal graph link prediction,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Efficient neural com- mon neighbor for temporal graph link prediction,

Reference 9

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Observation 5efdddb8-e83c-4933-be60-00f731ae07f9 · outbound

This paper cites Future link prediction without memory or aggregation,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Future link prediction without memory or aggregation,

Reference 10

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Observation 1923ef83-ca18-44b8-9b83-d4b8839ad4f6 · outbound

This paper cites TAMI: Taming heterogeneity in temporal interactions for temporal graph link prediction,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs TAMI: Taming heterogeneity in temporal interactions for temporal graph link prediction,

Reference 11

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Observation 414ca9bb-349f-4034-9af3-7bd152019710 · outbound

This paper cites Predicting dynamic embedding trajectory in temporal interaction networks,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Predicting dynamic embedding trajectory in temporal interaction networks,

Reference 12

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Observation f806c860-82e6-445c-bc7a-3269917fe614 · outbound

This paper cites Repeat-aware neighbor sampling for dynamic graph learning,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Repeat-aware neighbor sampling for dynamic graph learning,

Reference 13

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Observation 13b710a6-4952-430d-bee7-bcae03c9ccd1 · outbound

This paper cites Improving temporal link prediction via temporal walk matrix projection,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Improving temporal link prediction via temporal walk matrix projection,

Reference 14

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Observation f40c6a16-7ba7-498b-859f-4f6b6ba9a05c · outbound

This paper cites Towards ideal temporal graph neural networks: Evaluations and conclusions after 10,000 gpu hours,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Towards ideal temporal graph neural networks: Evaluations and conclusions after 10,000 gpu hours,

Reference 15

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Observation 5d7f5571-1a8c-44b0-a6d6-b7f33890ea2f · outbound

This paper cites Ranking on dynamic graphs: An effective and robust band-pass disentangled approach,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Ranking on dynamic graphs: An effective and robust band-pass disentangled approach,

Reference 16

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Observation 8541bf21-99bf-4805-a0fb-7620f89ce4ca · outbound

This paper cites Dyrep: Learning representations over dynamic graphs,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Dyrep: Learning representations over dynamic graphs,

Reference 17

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Observation 1408438b-efb2-4c07-b69e-9e6f7971fff4 · outbound

This paper cites Do we really need complicated model architectures for temporal networks?.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Do we really need complicated model architectures for temporal networks?

Reference 18

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Observation cdba590e-5a5e-4e23-8f1c-16199fb82b04 · outbound

This paper cites Neighborhood-aware scalable temporal network representation learning,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Neighborhood-aware scalable temporal network representation learning,

Reference 19

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Observation 793f097d-d522-4f81-9bd6-ff5bc18ed96a · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,

Reference 20

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Observation f8f292b9-eaf3-445a-89e5-536ff90582ad · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation,.

Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs Strictly proper scoring rules, prediction, and estimation,

Reference 21

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Pith citing papers

No inbound Pith citation observations are available.