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Paper Citation Record · LEDGER

On the Power of Heuristics in Temporal Graphs

As of 11 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2502.04910.

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

pith.paper-citation-record.v1
2502.04910 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T21:05:08.099495Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:09:39.803775Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy22
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c49efd29-b3f9-4c24-87d6-824250fc358f · outbound

This paper cites write newline.

On the Power of Heuristics in Temporal Graphs write newline

Reference 1

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unresolved
no resolver link, observed 2026-08-08T21:05:07.995940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:07.995940Z digest=sha256:722dce7125803a81ed725bebeb6375463922b00b1ecbb2fca18fbda04516b99b

Observation 559be85f-746a-4d57-9068-0c356fc9cb69 · outbound

This paper cites Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework.

On the Power of Heuristics in Temporal Graphs Bringing light into the dark: A large-scale evaluation of knowledge graph embedding models under a unified framework

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.776876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation c7eca132-26a4-444d-99fb-2e47ede171a0 · outbound

This paper cites Bias and debias in recommender system: A survey and future directions.

On the Power of Heuristics in Temporal Graphs Bias and debias in recommender system: A survey and future directions

Reference 3

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unresolved
no resolver link, observed 2026-08-08T21:05:08.004468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3defc9ca-9973-4c08-88f0-24a26704c041 · outbound

This paper cites Do we really need complicated model architectures for temporal networks? In The Eleventh International Conference on Learning Representations, 2023.

On the Power of Heuristics in Temporal Graphs Do we really need complicated model architectures for temporal networks? In The Eleventh International Conference on Learning Representations, 2023

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.759183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a167497a-2678-4dbd-a2a0-a44c1010c468 · outbound

This paper cites A new data structure for cumulative frequency tables.

On the Power of Heuristics in Temporal Graphs A new data structure for cumulative frequency tables

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.748672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.016250Z digest=sha256:055622680cbf8510ba2b662d0b194d29bc6556b834a2da7074594d93bf5852b3

Observation 857345e4-0b44-4f73-a4d4-fc90a0e36feb · outbound

This paper cites Long range propagation on continuous-time dynamic graphs.

On the Power of Heuristics in Temporal Graphs Long range propagation on continuous-time dynamic graphs

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.738817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 73fe9920-b5a2-4445-b0bb-dc90275aceee · outbound

This paper cites Benchtemp: A general benchmark for evaluating temporal graph neural networks.

On the Power of Heuristics in Temporal Graphs Benchtemp: A general benchmark for evaluating temporal graph neural networks

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.728839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.024366Z digest=sha256:ffc3abe57ce32bb72fb38120f2769eaf3c802fe425fa799a5dce6eed82a6db3a

Observation 38ffa7d7-5ef5-4ead-8a02-5afa1ab61153 · outbound

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

On the Power of Heuristics in Temporal Graphs Temporal graph benchmark for machine learning on temporal graphs

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.719485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.028139Z digest=sha256:9711b32ee105cc66d915ebf50fc16ad57e18b5d13ad67fac77099b289c3abe18

Observation 29f2d414-9152-4f84-a138-64c6787a1934 · outbound

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

On the Power of Heuristics in Temporal Graphs Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.709968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a3fb8079-6c7b-49c8-affb-e20eb7a4bc93 · outbound

This paper cites A survey on popularity bias in recommender systems.

On the Power of Heuristics in Temporal Graphs A survey on popularity bias in recommender systems

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.699918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation b24b10bb-c68a-43e4-8c91-f1d942c2ec6b · outbound

This paper cites On sampled metrics for item recommendation.

On the Power of Heuristics in Temporal Graphs On sampled metrics for item recommendation

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.690656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.038391Z digest=sha256:1fccab3fe8dff0774a85b1ebde0aaa54fe945ce388ec0078897df76822acdf1a

Observation 3e3c188c-1a49-4fcc-a4a9-b29b032afe8f · outbound

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

On the Power of Heuristics in Temporal Graphs Predicting dynamic embedding trajectory in temporal interaction networks

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-08T21:05:08.041630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:08.041630Z digest=sha256:a8309e978a0afeaf81caf4a594c6d52e50943c6333d6dfbabcc6f9bbe753c1cb

Observation 837b85c2-68ce-4b3e-858c-28b3a85198df · outbound

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

On the Power of Heuristics in Temporal Graphs Predicting dynamic embedding trajectory in temporal interaction networks

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.680811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation af1a5177-2ad4-4ef0-850a-e0895bb20a59 · outbound

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

On the Power of Heuristics in Temporal Graphs Neighborhood-aware scalable temporal network representation learning

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.671286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.047996Z digest=sha256:bd6e480a23b793eb3fb381ec26a85431e08aa178afdaefbb76cefcafcbf0df69

Observation f9b92182-6bb4-4f63-86fc-b43631ed91a7 · outbound

This paper cites Mixture of link predictors on graphs.

On the Power of Heuristics in Temporal Graphs Mixture of link predictors on graphs

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.662195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.051057Z digest=sha256:85852f60c88dc87db0807805e4bcc614a62ca1c6176b8d5ea9647f595d44de02

Observation eac65dcc-c0ca-4e89-8f75-55e9e0e40b82 · outbound

This paper cites Towards better evaluation for dynamic link prediction.

On the Power of Heuristics in Temporal Graphs Towards better evaluation for dynamic link prediction

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.653362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.054460Z digest=sha256:42326baae18fb87cb8d4cbe674b7d1f33072c077aae84e6b713b5b153f40e299

Observation 2d8670b8-4258-4d56-94a9-d7c8107146d7 · outbound

This paper cites A strong node classification baseline for temporal graphs.

On the Power of Heuristics in Temporal Graphs A strong node classification baseline for temporal graphs

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.644754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.057524Z digest=sha256:6830eb87ac5dc0494b2ce4adca697df127194605d3a8a56af674dd23f9ccff55

Observation a36e1893-917d-45af-9550-a5c101ee1c91 · outbound

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

On the Power of Heuristics in Temporal Graphs Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T21:05:08.060637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:08.060637Z digest=sha256:8b14c3194c5ef742768370a6bdc5b5ebf43e0a0e4b0b617e6182c7e07556d3a7

Observation 5bc12c2d-5f26-4afb-8eaf-bf5b1f70f1a5 · outbound

This paper cites Temporal graph networks for deep learning on dynamic graphs.

On the Power of Heuristics in Temporal Graphs Temporal graph networks for deep learning on dynamic graphs

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.635310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.064557Z digest=sha256:e3dc19f4e9bbba18f6ee966f7af7c9700a789439c719dd1cc98e3e5d4513e9b9

Observation e62ae758-d8bb-4404-9500-8743aee3e651 · outbound

This paper cites Temporal graph analysis with tgx.

On the Power of Heuristics in Temporal Graphs Temporal graph analysis with tgx

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.625619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a3d4ef85-163e-474b-927e-5f6540faf81c · outbound

This paper cites Dyrep: Learning representations over dynamic graphs.

On the Power of Heuristics in Temporal Graphs Dyrep: Learning representations over dynamic graphs

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.615406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.069986Z digest=sha256:69f1117c2e9fda5b3be56586c5469b26b1272a2d7806e39b2d1eb63e42734666

Observation 1c2f9501-203c-4d64-b81a-fa9638f64c2c · outbound

This paper cites TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning.

On the Power of Heuristics in Temporal Graphs TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-08T21:05:08.072813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:08.072813Z digest=sha256:2ee8bd24ac217d90ce1e489b9397d2cfbde30551cb665cce0417d89fdd8802a3

Observation 5e8e0747-5d2a-430f-ba4d-2ef29782f8e9 · outbound

This paper cites Inductive representation learning in temporal networks via causal anonymous walks.

On the Power of Heuristics in Temporal Graphs Inductive representation learning in temporal networks via causal anonymous walks

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.603988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 742df5ec-fb76-42be-8e48-5191b7c74786 · outbound

This paper cites A survey on the fairness of recommender systems.

On the Power of Heuristics in Temporal Graphs A survey on the fairness of recommender systems

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.593307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 1ec4e6cd-eaa6-4c63-9a15-13a89362f681 · outbound

This paper cites On the feasibility of simple transformer for dynamic graph modeling.

On the Power of Heuristics in Temporal Graphs On the feasibility of simple transformer for dynamic graph modeling

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.582698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.081297Z digest=sha256:a1be952d057dab0d4359d56a33af62b003507252f4b50ace008f6beb4d45c823

Observation 203e8a82-fe58-4e0c-ac62-558a43c79dd8 · outbound

This paper cites Inductive representation learning on temporal graphs.

On the Power of Heuristics in Temporal Graphs Inductive representation learning on temporal graphs

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.572357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.084048Z digest=sha256:1940b4c3c23f61366af01717d7021e67f95a070b0be4149ba7f61fc551e99be0

Observation a34700b2-d9b3-4a61-aed7-322c6b391571 · outbound

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

On the Power of Heuristics in Temporal Graphs Towards better dynamic graph learning: New architecture and unified library

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T21:05:08.561781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.086802Z digest=sha256:54d70c1f1dbb76cb7b3d1ca089df92443dbe5e5153cb65ef90ff5bfcfeecae8d

Observation 6bec8e8d-5c41-41bc-8594-e9576e92a491 · outbound

This paper cites Efficient neural common neighbor for temporal graph link prediction.

On the Power of Heuristics in Temporal Graphs Efficient neural common neighbor for temporal graph link prediction

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-08T21:05:08.089339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:08.089339Z digest=sha256:cd263a5cea7f599ec8c91754cf240c21bc8f1d7c884374ca42e38adae3212e33

Observation 7687732e-8d48-4ae7-af00-b05a6fbda75e · outbound

This paper cites @esa (Ref.

On the Power of Heuristics in Temporal Graphs @esa (Ref

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-08T21:05:08.092458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:08.092458Z digest=sha256:1c109513698ccb854ab9d7291291596860caf5b45c0fb79ea93b401cef8c6673

Observation 179a0c0c-4ea3-4e3c-ad8f-a9a656c435bb · outbound

This paper cites an unresolved cited work.

On the Power of Heuristics in Temporal Graphs Unresolved cited work

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-08T21:05:08.096128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:05:08.096128Z digest=sha256:6ef473891d5e5915c44e1fb084b769eeb4f5ca71d368b86b4e0d877c00cc620b

Observation c9eab355-7db4-4500-a19d-73e6b0833f7c · outbound

This paper cites Temporal graph models fail to capture global temporal dynamics.

On the Power of Heuristics in Temporal Graphs Temporal graph models fail to capture global temporal dynamics

Reference 32

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T21:05:08.536168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.099495Z digest=sha256:63eb8e98a67cfe2a3caca6a79a787f42a9990f087ed41dfebe80e3c5be0908c1

Pith citing papers

Observation a4d652d7-08e9-4c29-9808-ca0de76e3597 · inbound

Are Large Language Models Good Temporal Graph Learners? cites this paper.

Are Large Language Models Good Temporal Graph Learners? On the Power of Heuristics in Temporal Graphs

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T11:09:39.803775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:09:39.803775Z digest=sha256:d183172fc23ecc7808dcebaf735c1741085497ac74d91b22949343232dfb6490

Observation 101f2cf1-93f7-4720-94bb-d02f43ef4fd6 · inbound

Maximizing Reachability via Shifting of Temporal Paths cites this paper.

Maximizing Reachability via Shifting of Temporal Paths On the Power of Heuristics in Temporal Graphs

Reference 231

Resolution
verified exact
arxiv_id, observed 2026-05-13T05:02:17.264771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-13T05:00:02.957536Z digest=sha256:1a70f5c484c04bd63ad8fd87e961b279b2a074ad76ac6b9478520ec44ea96acf