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Paper Citation Record · LEDGER

On the Power of Heuristics in Temporal Graphs

As of 20 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 2026-08-19T06:32:44.657259+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=sha256:5033a0071824ff8d79f7496b9773ed26e266142733fabba23c8eb950e95ed271

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

Resolution
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 2026-08-19T06:32:44.657259+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.

source=arxiv_source observed=2026-08-08T21:05:08.004468Z digest=sha256:c25e1f3492dfc45a8260bd813d3bed8bcc745733388462ad825aea9fdaf94df8

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.008301Z digest=sha256:5d116fd5d30c0bdea580617189d6b6fb13362b0a1db1b53e6425328ae4f9c1e7

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 2026-08-19T06:32:44.657259+00:00.

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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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.024366Z digest=sha256:a5ad3d35518f3248c9ab5fad3ff8e5183a6e103c4394721495d1bde87f0555b1

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 2026-08-19T06:32:44.657259+00:00.

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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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.031836Z digest=sha256:cc2a32ce3f106136e51d98a4f790fc6a1e476cdc0cac809bef6900bcec96d95f

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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.038391Z digest=sha256:b1583e9ef351baed7bb936770e4fd31150347b33d118c1090289d47065b5c3f8

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=sha256:569573e41cddf784107c1dbdf689e59b2b91f9b6407541d09c8931623e6cdb2d

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.044803Z digest=sha256:570d82c691885e06279fb688d0f2f025dea13df7e71f41cc1fb13078f9e5f3ec

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.047996Z digest=sha256:c2d2bdde43835d5158938f7a9922fef0fa5c2ab1868e2a635d126476d73db6cb

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 2026-08-19T06:32:44.657259+00:00.

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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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.054460Z digest=sha256:a9aea24ead8f1429adeadacd29920e2dc6fe7acd13bf6334573656ab0cc8ee77

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.057524Z digest=sha256:9c30799f21a1782f2c8f58b34beb7a58d82d51192cb016ffd2cc55d895580579

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=sha256:8c64afcd2c6f116803abadabb75e71980fde777f1cb5172ef1e56dce2048a5c5

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.064557Z digest=sha256:44ab8755050601b3349d179965c20757f02c6a5251632972f9211496fee83885

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.067187Z digest=sha256:6279e97cff79ec98d8069124cfd38da93f00cb25285011d7b24fa5253489bad7

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.069986Z digest=sha256:fe7a8d8f4925d80e7d7de6372f45f52bec124ecaedd1b5734a72d6b78cbf3008

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=sha256:90fc0f1174a0364185da3c522ae945c2028abfc7019c2faf691e4f0d5f71e407

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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.078584Z digest=sha256:9b05ccacefd5def7e78609873a70e656c7658c0ee4dddf8438d24a93dd7ea6bb

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.081297Z digest=sha256:f3fe12674df146b592dce47a976db1626bc8c35fa6a94a3412d04d943c7db71b

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.084048Z digest=sha256:fc516af20454874c841a66776961544fe2d3aab6a18617ffbbcbb5b03cff757b

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.086802Z digest=sha256:e23885e9937f00b0a3afeab98123e6b88fa21fb9ce56025f5b79ce40bd4cabe8

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=sha256:4d0abcb1f371839e70bfcd15c3df3c94a48f0d32f5dc14d640856f77121e5cb5

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:d4d88f48dba8dcc455b18c0efd98562aca116b9931a6ad2c2049291103baedda

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=sha256:2f343e3f03b156a3f2b4bca26a8690a140063c5f81aa205cd4a0016f33687159

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T21:05:08.099495Z digest=sha256:5f6e7dfbf041c1560bda0a9517211fc7f0a03c02686599764a296ebbce7213b4

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:c31880a9d49aefcde8abbc1cc9e496dff5a08f6925559d3406ce2dcee580b621

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-13T05:00:02.957536Z digest=sha256:f85eb2c789769bf1bc066e76dabd02ba527137067c88ed5e3e2b4296265798aa