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Paper Citation Record · LEDGER

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks

As of 14 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2412.06173.

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

pith.paper-citation-record.v1
2412.06173 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:00:34.395601Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T00:58:31.100417Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T01:00:51.523686Z

Reference resolution

50 of 50 outbound references displayed

  • verified exact1
  • verified fuzzy29
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22a11b61-75ed-4c99-ac9a-b56091fe4bf2 · outbound

This paper cites On the bottleneck of graph neural networks and its practical implications.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks On the bottleneck of graph neural networks and its practical implications

Reference 1

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no resolver link, observed 2026-08-11T20:00:34.254511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.254511Z digest=sha256:690852539e48390364f03b1b25834fe7a80463f533c47dec15a16a76fcdad9e8

Observation 1c8ce0d8-c254-49fb-a137-00034e7ce6ff · outbound

This paper cites Diffwire: Inductive graph rewiring via the lovasz bound.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Diffwire: Inductive graph rewiring via the lovasz bound

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.915495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.257456Z digest=sha256:d5d58fb0657607eefb33ad0b404057c8f43fe871c316f246993540abdbbb7940

Observation a91c6f5a-d957-4a2f-bb02-694d7a3a9c37 · outbound

This paper cites Graph neural networks use graphs when they shouldn't, 2024.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Graph neural networks use graphs when they shouldn't, 2024

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.905121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.260080Z digest=sha256:3b588ed51ee098fa1c1e39e7d4f1fccc80af1111aa8f0732a08f6d935fdf3b00

Observation 6affe739-ccd3-4747-946e-7a1180544703 · outbound

This paper cites Understanding oversquashing in gnns through the lens of effective resistance.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Understanding oversquashing in gnns through the lens of effective resistance

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.895609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.263070Z digest=sha256:44ab2f20fb4e035b8497f0c2d8f432f925e5cda56d24f3596a26d6510ba5c10d

Observation b921d7c0-574e-4182-b5be-c29803e918e4 · outbound

This paper cites Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

Reference 5

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no resolver link, observed 2026-08-11T20:00:34.265717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.265717Z digest=sha256:7cc89580a0c1d9b78bd1fa16fbc986f3b45f06aa29884714c01e5119fb84af22

Observation c8f481c2-b2ce-491e-8722-a6dc30734ef2 · outbound

This paper cites Bronstein, Joan Bruna, Y.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Bronstein, Joan Bruna, Y

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.885756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.268855Z digest=sha256:c56cc1fbed67e53d317a253ae160d6babf9dfc9d55227f64758c7574233c737c

Observation b4228aa7-b75e-430e-9029-161f6e4a7d57 · outbound

This paper cites Hyperbolic graph convolutional neural networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Hyperbolic graph convolutional neural networks

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.877666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.271710Z digest=sha256:f6230ff3b61bebfea04794e8c47b7dc28cf560a3369c77c55b2f92c453051d3c

Observation a1c3a042-cced-48e8-bee2-e4525bbc1fea · outbound

This paper cites Fully Hyperbolic Neural Networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Fully Hyperbolic Neural Networks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.274020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.274020Z digest=sha256:6bdfb8ffbdcbae15de6d4af3d979cb9f4154f389e28c63c8b83db6413901daf0

Observation cb711827-c919-44e8-bad2-9abd9f53798e · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Convolutional neural networks on graphs with fast localized spectral filtering

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.869699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.277380Z digest=sha256:afcd9b1ec99d48699f58876c76ceac0c569ecc6446ad6e064bb6307ba7dcd1ce

Observation 42e82e29-1c38-46df-b61f-c93c0517d5c7 · outbound

This paper cites Bronstein.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Bronstein

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.860493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.280243Z digest=sha256:05a943cd39400ea79dafe6c95c5ce832f79c75aa68d25e552297fe5b2249d9c7

Observation 1052fd9d-50e6-4e88-925f-c17d4d36b9d8 · outbound

This paper cites Hyperbolic neural networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Hyperbolic neural networks

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.850791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.283253Z digest=sha256:e7df2d518b64bce4cd972f2fa6681c745fc5188b81ebbd90917dfb85ede26379

Observation 22043c32-0f1c-4fea-82d9-4cc2c2223756 · outbound

This paper cites Diffusion improves graph learning.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Diffusion improves graph learning

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.842031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.286101Z digest=sha256:d6e71ebda54825d77fc9f3ba038c2ae4b91628190e2b0f9581f33a6152d41fbc

Observation 2ca3eaed-cb21-4ab4-ba6d-acf86d45b12e · outbound

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

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Hamilton, Zhitao Ying, and Jure Leskovec

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.832881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.289147Z digest=sha256:31a3a71f48e7a884a346652d740764acbe4160e10fe5f9f61adac797d10c8212

Observation b44914ae-2f9a-4878-a9f7-536ef69f7f0d · outbound

This paper cites Harris, K.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Harris, K

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.823989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.291877Z digest=sha256:9c8360f19a4a35bcf0d741ec12f238876ac963c7839680b2df02bdab2e58976c

Observation 00b1bb22-49b0-45c2-af3e-04654e723a20 · outbound

This paper cites Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.294777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.294777Z digest=sha256:bdd4916eb4fc75e5b99d4e575a1523b04944e59ff4ddceb8d636a8cfcb858323

Observation ee54581c-8003-4128-864b-11d3242f6c4e · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.298186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.298186Z digest=sha256:7d6f34ed0577d6384bbf784404e5b097dd04bfea0d955f36c22556113fa50e81

Observation 5a04ce65-9eda-4281-999b-5569a4d5b1a2 · outbound

This paper cites Combining Label Propagation and Simple Models Out-performs Graph Neural Networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.301665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.301665Z digest=sha256:acacd9ebb5dcf3ccd50ff2e846bb7ec4e40abfdbba0ba0fa8df56820c4647466

Observation bbde50c4-b664-4f94-8b61-aa6fdfefde13 · outbound

This paper cites Stevenson, and Lizhen Lin.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Stevenson, and Lizhen Lin

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.815139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.305023Z digest=sha256:ce87f4770276748b285143950f34df55b98a51a700f7d3bbdf0c84c2989fd5d7

Observation 221673f8-d877-40bf-a221-5fb4153188c7 · outbound

This paper cites Banerjee, and Guido Montufar.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Banerjee, and Guido Montufar

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.308020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.308020Z digest=sha256:fd31e2620e71e511afa2ce8265e67efc28bc836709bca4f78fec882cc8cc1316

Observation 16ea4704-145e-4da5-bcc1-f6a171e2f412 · outbound

This paper cites Shedding light on problems with hyperbolic graph learning.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Shedding light on problems with hyperbolic graph learning

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.800124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.311188Z digest=sha256:9a968cdf019b37f8bf7a064f96d0a45da3820109b8e343f62c2e19bea310f410

Observation 23c7c7a3-898e-4dfe-a51d-a215c4d9869c · outbound

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

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Semi-Supervised Classification with Graph Convolutional Networks

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.313818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.313818Z digest=sha256:c8b95aa99c3ac2f3f5ba8a532ed8d378cb516cd767cfc204a1c9b74dd61e09af

Observation 1ce9958b-f130-4330-b699-7aca85a3b7e6 · outbound

This paper cites Kleinberg.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Kleinberg

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.791123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.316948Z digest=sha256:56add9ac4ff4df4945d6e5d3a161d0577b629f9e7cacf7b7c8b58a0aa6af2b81

Observation ef74f5a8-4b7f-4338-aae8-7a2cf030f8ad · outbound

This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Predict then propagate: Graph neural networks meet personalized pagerank

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.781942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.320046Z digest=sha256:8de2bd6a52e001ad7135eac2a6ee72526d8975786c79329a87a68daadaab1ee9

Observation cf6effd8-d329-4b02-bba9-fa6b6c6f11b3 · outbound

This paper cites Towards deeper graph neural networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Towards deeper graph neural networks

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.772434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.322834Z digest=sha256:8856368781825d4daa87b1c924858e098103e431c3386de0569b7fa4063434ad

Observation bff719e0-66ff-4b7b-a4b3-0c28ab1676e3 · outbound

This paper cites Differentiating through the fr\'echet mean.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Differentiating through the fr\'echet mean

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.763358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.325658Z digest=sha256:8e5d74a5fbf2a92c1af7e3397e4db3864715a379489544f8d291ac56a2afbd77

Observation 46d55731-2115-47d2-bcd0-63e3b30e0d6b · outbound

This paper cites Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.328301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.328301Z digest=sha256:2a447f01c176d0ef472d6e4c582589a018a55c168e898a58e5d59886ca28de46

Observation 82946a4a-ae0b-42ca-aa11-07efeab1ba73 · outbound

This paper cites Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-08-11T20:00:34.564193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.331434Z digest=sha256:98b9ec2f69cb5cda7ffc72e4377e82902511f3dcaf0627634945ef4d7e155c5c

Observation 23fbdfc1-8f49-438b-8498-f095da3ae1ea · outbound

This paper cites Boscaini, Jonathan Masci, Emanuele Rodol \`a , Jan Svoboda, and Michael M.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Boscaini, Jonathan Masci, Emanuele Rodol \`a , Jan Svoboda, and Michael M

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.754402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.334845Z digest=sha256:7480b8f5715ef07bc9b79520a64171de3ca3077f1a9739bfe69983b42a6c072e

Observation c5fedc2f-b5b3-465c-a36f-e0a05746ddad · outbound

This paper cites Revisiting over-smoothing and over-squashing using ollivier-ricci curvature.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Revisiting over-smoothing and over-squashing using ollivier-ricci curvature

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.745945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.337511Z digest=sha256:dcb56d878b39d03b55b564895b28acdfefab92a878e40972df9e8c2a54d882d4

Observation 8b7e881b-cb52-49bd-86ac-be8a660ea27f · outbound

This paper cites Poincar \'e embeddings for learning hierarchical representations.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Poincar \'e embeddings for learning hierarchical representations

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.737702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.339842Z digest=sha256:fbb1b2ea6a2317b199068621fce43960e29a8def0b23d93bb4f49584373ae2da

Observation 3a8919c9-3c94-49b6-bc27-03145fe3ce29 · outbound

This paper cites Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.342079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.342079Z digest=sha256:81c413cadc7a8526c5b0c19de7c9f2f41ce869a510829ac68a8e11498c491f0f

Observation a56c2dcd-b714-43e9-ae2f-fbce03b5dd8e · outbound

This paper cites Multi-scale Attributed Node Embedding.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Multi-scale Attributed Node Embedding

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.344533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.344533Z digest=sha256:ad9828bca888fa83d9e00b74e79be9e295cd6a308f02242af3d6c5e427fdffc4

Observation c01acd07-7e8e-47f4-a5db-c08ffc6cfa91 · outbound

This paper cites Konstantin Rusch, Michael M.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Konstantin Rusch, Michael M

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.347509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.347509Z digest=sha256:17d3e8ab4bcc19fd0c4c4f47008757354533198842f8f51271a238f1e4b1a55d

Observation b917894b-515d-4c7d-9bd1-308588e7bdbb · outbound

This paper cites Low distortion delaunay embedding of trees in hyperbolic plane.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Low distortion delaunay embedding of trees in hyperbolic plane

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.724105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.349871Z digest=sha256:acff9cb62ab6c3df7fb364eb8b88d1af4bf3883ab1c9a8bf3f0ce047cbe9c5c6

Observation a918c9aa-10ad-40df-886c-59b4feb1e78e · outbound

This paper cites Collective classification in network data.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Collective classification in network data

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.715338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.352248Z digest=sha256:9ecf27e3364775d3cad9fbbfc48e24f7fcdf132469ecd441fa884ce21e27aa1a

Observation be80fcb1-0a58-4727-a531-b1c99bebb3ef · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Pitfalls of Graph Neural Network Evaluation

Reference 36

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Unavailable: canonical work link unavailable.

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Observation e7888761-96f3-49f7-af4d-48d3a743a3a6 · outbound

This paper cites Venkatachalam, Danica J.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Venkatachalam, Danica J

Reference 37

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ad051392-bf4d-4ba4-8249-a26010de58cf · outbound

This paper cites Relwire: Metric based graph rewiring.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Relwire: Metric based graph rewiring

Reference 38

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation a52db9d3-605d-4838-8af3-fda337dba70f · outbound

This paper cites Semi-supervised learning (chapelle, o.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Semi-supervised learning (chapelle, o

Reference 39

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 729b42e2-d5f1-41d8-9121-39c92270d459 · outbound

This paper cites Is rewiring actually helpful in graph neural networks?, 2023.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Is rewiring actually helpful in graph neural networks?, 2023

Reference 40

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ede12201-1e59-4312-843c-25585b150db1 · outbound

This paper cites Graph Attention Networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Graph Attention Networks

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 307f6096-c3da-45bc-8ed3-cd39ebb27f47 · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 42

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.372348Z digest=sha256:ee697ae76a370be1437ea9d2af6867ca24b59d06b9f6f717efafef8e6d2e4db3

Observation 7031beac-1523-4608-b59e-951b2de83f1e · outbound

This paper cites Watts and Steven H.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Watts and Steven H

Reference 43

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation df5c6d59-aa17-4192-9a3e-560785be102a · outbound

This paper cites Simplifying Graph Convolutional Networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Simplifying Graph Convolutional Networks

Reference 44

Resolution
unresolved
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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.377872Z digest=sha256:8f163f82f4e95c046fd0061511640aad3c3a16b4734e19f4ddd08a22d81824c0

Observation 8b87c9f9-ddfa-4bf6-b988-870e937dc379 · outbound

This paper cites an unresolved cited work.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Unresolved cited work

Reference 45

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 75b9db1f-dea7-4a57-886f-482b6f33264e · outbound

This paper cites How Powerful are Graph Neural Networks?.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks How Powerful are Graph Neural Networks?

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.383685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.383685Z digest=sha256:82dc1d84e227052c2d6b4bb856e0e3d3ccc9b3ae745f24c6bfc78bb0bc26b3c7

Observation e61487ba-1ea3-4ef4-8b03-0df70eaef3a8 · outbound

This paper cites Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T20:00:34.386889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.386889Z digest=sha256:e7950357559841b44cec7feb9582e6f169317a108071a6cac0236c8fe1bc50db

Observation 813daed5-a55c-4607-a0cc-cbd80cf4acfc · outbound

This paper cites Hyperbolic graph attention network.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Hyperbolic graph attention network

Reference 48

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 8f988105-61ca-4cdd-82cd-689281475ef7 · outbound

This paper cites Lorentzian graph convolutional networks.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Lorentzian graph convolutional networks

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-11T20:00:34.637319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-11T20:00:34.392658Z digest=sha256:ed0f2e6f9bab42f7952b2f0ebd8835ac84cee73ad97a476d0e283e602e51df73

Observation de446992-3c78-4847-ab50-708fd087c4fe · outbound

This paper cites write newline.

Revisiting the Necessity of Graph Learning and Common Graph Benchmarks write newline

Reference 50

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:00:34.395601Z digest=sha256:562fccd9c1a5d90b1dd07b8fc789c3da1a0b0ef39b121cf5112baa107ab9f0f1

Pith citing papers

Observation 428d2124-3d0a-43d5-b0ef-585871fee8d9 · inbound

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence cites this paper.

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence Revisiting the Necessity of Graph Learning and Common Graph Benchmarks

Reference 100

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation b6e429be-f7f6-4ff7-af27-29041629732c · inbound

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence cites this paper.

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence Revisiting the Necessity of Graph Learning and Common Graph Benchmarks

Reference 100

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verified exact
arxiv_id, observed 2026-05-22T01:00:51.526727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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