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Paper Citation Record · LEDGER

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models

As of 20 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2412.15496.

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

pith.paper-citation-record.v1
2412.15496 v3

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:32:21.896732Z

measured 48 of 48 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 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

48 of 48 outbound references displayed

  • verified exact1
  • verified fuzzy27
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efedb952-f54c-41c1-91e7-89dc84bffc8c · outbound

This paper cites Community detection and stochastic block models: recent developments.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Community detection and stochastic block models: recent developments

Reference 1

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unresolved
no resolver link, observed 2026-08-11T11:32:21.580286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.580286Z digest=sha256:11f077b329fa102ff1d37b4241015b98e71b59ec9093731c13c362f74c5e56d8

Observation dc7d7823-020d-4598-a2f4-025bf0ca9915 · outbound

This paper cites and Sandon, C.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Sandon, C

Reference 2

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6fb5a7eb-f951-4609-86bb-550e4f8f093f · outbound

This paper cites S., and Hall, G.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models S., and Hall, G

Reference 3

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation c0a40dcc-ea75-49a3-b7b0-c61863cd9c48 · outbound

This paper cites Almost Surely Asymptotically Constant Graph Neural Networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Almost Surely Asymptotically Constant Graph Neural Networks

Reference 4

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local_arxiv, observed 2026-08-11T11:32:22.193564Z

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 1cf51039-9328-47ea-95e7-117bcbe99bb8 · outbound

This paper cites Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization

Reference 5

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1b458fc9-63d7-442d-a639-d3cd35130836 · outbound

This paper cites Effects of graph convolutions in multi-layer networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Effects of graph convolutions in multi-layer networks

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.876542Z

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 3e99a336-5458-43a3-a66d-3c31b977bcfb · outbound

This paper cites An iterative clustering algorithm for the contextual stochastic block model with optimality guarantees.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models An iterative clustering algorithm for the contextual stochastic block model with optimality guarantees

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.858100Z

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=2026-08-11T11:32:21.621098Z digest=sha256:1bdd8569b30eb0d21cf1ba60f18f35e58e07745094d52d7fee8eee09e44125be

Observation 14bb32cd-9601-4a98-8b97-a8a2d192368f · outbound

This paper cites Supervised community detection with line graph neural networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Supervised community detection with line graph neural networks

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.837394Z

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 2eb6d217-8219-4e2d-8c6c-1ed15e817109 · outbound

This paper cites Contextual stochastic block models.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Contextual stochastic block models

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.819388Z

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 8bb4b8af-02b7-4edf-a1f8-b74d0f2c6cbd · outbound

This paper cites Exact recovery and bregman hard clustering of node-attributed stochastic block model.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Exact recovery and bregman hard clustering of node-attributed stochastic block model

Reference 10

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 987c0809-d1d7-4702-a102-d55b54f29beb · outbound

This paper cites and Zdeborova, L.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Zdeborova, L

Reference 11

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 0887373c-f15f-4bb1-8fde-3152f6b0700a · outbound

This paper cites an unresolved cited work.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Unresolved cited work

Reference 12

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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 749962b2-269c-4693-80fb-a0d02a62679f · outbound

This paper cites Graph neural networks for social recommendation.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Graph neural networks for social recommendation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.656172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7260532f-1334-43bb-83d3-6ace382af5e2 · outbound

This paper cites and Lenssen, J.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Lenssen, J

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.735288Z

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 a522dfa1-f674-4bc9-9fd9-a75071d14e42 · outbound

This paper cites Graph attention retrospective.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Graph attention retrospective

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation abb0e452-1032-4bab-9d1c-9611e7858a29 · outbound

This paper cites and Karo \'n ski, M.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Karo \'n ski, M

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.700769Z

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=2026-08-11T11:32:21.673920Z digest=sha256:c73221b2b359587c48c66348bb4045cb7a3d3ba9d6c8d6c3a60cbcd8bf82c926

Observation 7abc0bbc-e401-40d7-9274-2e36692ee38a · outbound

This paper cites D., Kosciolek, T., Leman, J.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models D., Kosciolek, T., Leman, J

Reference 17

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unresolved
no resolver link, observed 2026-08-11T11:32:21.682393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b11cdb59-efe2-4dfb-8e73-9639bae5e6dc · outbound

This paper cites W., Laskey, K.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models W., Laskey, K

Reference 18

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unresolved
no resolver link, observed 2026-08-11T11:32:21.689072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.689072Z digest=sha256:9ae565b94a99cd0efe9adc51d7d0cb28916af770a2df9d3f9e2c345ed4f4a770

Observation bbd9c0e7-e67d-43f3-af7e-473951b84c70 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Open graph benchmark: Datasets for machine learning on graphs

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e60601d7-a418-45e9-9746-0cd6f83cd5fe · outbound

This paper cites S., Levi, A., and Valera, I.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models S., Levi, A., and Valera, I

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.642676Z

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 42d0db76-7a09-45fa-af49-035ad9a59947 · outbound

This paper cites Not too little, not too much: a theoretical analysis of graph (over) smoothing.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Not too little, not too much: a theoretical analysis of graph (over) smoothing

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.625541Z

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 e089a247-1b0d-4f1b-819b-568e272c1dff · outbound

This paper cites an unresolved cited work.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Unresolved cited work

Reference 22

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raw_fallback, observed 2026-08-11T11:32:22.604700Z

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=2026-08-11T11:32:21.717040Z digest=sha256:b186ab1c30418990c262e0e57b42c7bdf79bd279a4ce541d3d50ede9d7677746

Observation 1604990b-7f78-4b5e-94ef-13c7e4b150fe · outbound

This paper cites B., Rossi, R.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models B., Rossi, R

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.586384Z

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 75fc0671-30bf-4a48-9bb3-a43de8787cbc · outbound

This paper cites E., Rivest, R.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models E., Rivest, R

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.566573Z

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 3f0789b8-6a37-403b-bda3-32cc5f2eb70e · outbound

This paper cites Towards deeper graph neural networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Towards deeper graph neural networks

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.545683Z

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=2026-08-11T11:32:21.739694Z digest=sha256:2193f6c53c2a865fd27ac6f3826ddb45aa6c79e1e5489c63ff7cfe6d89311956

Observation de7bf6ca-b748-4a79-83e1-66147b196175 · outbound

This paper cites and Sen, S.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Sen, S

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.525264Z

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 16d14a71-b7a0-49d0-aeb9-ec00f867fd6f · outbound

This paper cites When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.502966Z

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 a9486c11-eda5-49d4-a089-1680e864a2eb · outbound

This paper cites Hyperspectral image classification using feature fusion hypergraph convolution neural network.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Hyperspectral image classification using feature fusion hypergraph convolution neural network

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.757383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.757383Z digest=sha256:194e530865e659fb032a3395fa17c93845c0e3b90e90c4075ba886bf1a72d91f

Observation 81ea639e-792f-4610-805e-ce0a5c806855 · outbound

This paper cites an unresolved cited work.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:32:22.484418Z

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 58eee9be-b24b-408e-91a6-c65944836e11 · outbound

This paper cites A Survey on Oversmoothing in Graph Neural Networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models A Survey on Oversmoothing in Graph Neural Networks

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.772614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.772614Z digest=sha256:f4c2071569434de129370b43af9c084f81ee594238e2206ab75e36dcb1d6a530

Observation a4e3e5dd-6fd0-405f-99aa-8bb3bb4bf593 · outbound

This paper cites Graph attention networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Graph attention networks

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.782796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.782796Z digest=sha256:74cfc74bd49a90932d2928cc2165d6f4c928057e57af916c55a67f674fab71cb

Observation 2e611079-8924-4eec-a221-22392ac5d235 · outbound

This paper cites Understanding heterophily for graph neural networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Understanding heterophily for graph neural networks

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.788194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.788194Z digest=sha256:12ae9b884f106f7cf68ef9b2bc8fc51cbf0f739d48c81c85ac25140a7590880e

Observation 8ea0c2b1-e305-47f5-9cbb-e35c6349e6be · outbound

This paper cites Graph attention convolution for point cloud semantic segmentation.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Graph attention convolution for point cloud semantic segmentation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.436910Z

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=2026-08-11T11:32:21.795060Z digest=sha256:fb67b41aa9c6dcdfadbba25f7127b1c5d721b61ce900770d6ff7d754196d1268

Observation 2ea15010-a720-4a63-890d-ec5b0379418f · outbound

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

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.804721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.804721Z digest=sha256:26148b935d28759f80ffe28addc62b4cb39ab655e943ca93b9bc074bcfadc69b

Observation a8300f00-a2f1-40f4-a70e-0c8f82e73fba · outbound

This paper cites Kgat: Knowledge graph attention network for recommendation.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Kgat: Knowledge graph attention network for recommendation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.414318Z

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=2026-08-11T11:32:21.810975Z digest=sha256:5266e469491cc305953ff310e58a9d35cd42ead02eb9ce7245f2d478c636cb0b

Observation b8235562-14d0-4574-8493-da80cb9cfc42 · outbound

This paper cites R., and Li, P.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models R., and Li, P

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.393357Z

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=2026-08-11T11:32:21.817178Z digest=sha256:6fe49f78524c1fa42d1c02f396cd7e147654450dd90f811dc170cf0202dfabd3

Observation 9f237d5d-4976-4102-a1f2-b14ecc2c67cc · outbound

This paper cites Graph neural networks in recommender systems: a survey.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Graph neural networks in recommender systems: a survey

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.372448Z

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=2026-08-11T11:32:21.823125Z digest=sha256:04be9c0ff3c95b7c246f40d94d0caa1a9605a85b7000ecc088017be6033b0954

Observation 64f069ac-100f-4c22-8437-ac065944a69f · outbound

This paper cites A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.829688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.829688Z digest=sha256:4c9302686e0d9901f17dd486bf28e6a64176a4aab29a164d9885b7301f52bdae

Observation 19bd5e62-4b0f-44be-beef-c72101f64f05 · outbound

This paper cites Demystifying oversmoothing in attention-based graph neural networks.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Demystifying oversmoothing in attention-based graph neural networks

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.350709Z

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=2026-08-11T11:32:21.836910Z digest=sha256:1dc632cbfc8addc68a3a856c0df15e0896b68bdf47d56bc9d8f86ae4b683cde8

Observation 565ac76a-787f-4910-8d73-270111423887 · outbound

This paper cites an unresolved cited work.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:32:22.331499Z

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=2026-08-11T11:32:21.842141Z digest=sha256:adcfb9e16267fbe007fe48906c3083972fc6c1a859095e0fba4013d779ddfb22

Observation 2fd76e25-0558-47b2-a036-7fc2a3d4558a · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations, 2018.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models How powerful are graph neural networks? In International Conference on Learning Representations, 2018

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.848506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.848506Z digest=sha256:b117fdcd8b485d403b3d33ef40bf5441d4d88539fc3d47b2729bc84fb0552fb6

Observation 3a0cdfc1-01f8-4eb4-bf56-69a6c042b54d · outbound

This paper cites Revisiting Over-smoothing in Deep GCNs.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Revisiting Over-smoothing in Deep GCNs

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.855510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.855510Z digest=sha256:b5630307585e9d4f98e12f7ce9f911137ed2f0d43a9ba1ed476f9c3c9bbfaa9f

Observation bb28ac96-dd0c-4c1d-9e97-01d133b1635d · outbound

This paper cites You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-11T11:32:21.959782Z

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=2026-08-11T11:32:21.861041Z digest=sha256:0cc9173f4d783cd6a65ebc880193849d7ffde184efbfa5b2a42524c9d082d50a

Observation 891d3094-0e4b-430f-83f5-343ef2385998 · outbound

This paper cites an unresolved cited work.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:32:22.299140Z

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=2026-08-11T11:32:21.869423Z digest=sha256:1b32d641d2a7cbd6e5de209f49a844eea453668415a650b8dc50371a88815024

Observation be66a49c-dc5b-4727-9e81-854574125d99 · outbound

This paper cites an unresolved cited work.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:32:22.272261Z

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=2026-08-11T11:32:21.876011Z digest=sha256:90b422975a7dc904a33f160d0520f31f6373a87a10df8177f0ab8f4dff3bbb6f

Observation 9bd91d3b-82e2-41ea-9d20-9de673628560 · outbound

This paper cites and Tan, V.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Tan, V

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.251613Z

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=2026-08-11T11:32:21.882011Z digest=sha256:e379f787c4b4069553505cef0805c8df455eed83163170a48ffae16905e74727

Observation cc23a366-87a1-4939-882f-468bf52b32c5 · outbound

This paper cites and Akoglu, L.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models and Akoglu, L

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:32:22.230853Z

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=2026-08-11T11:32:21.888930Z digest=sha256:bd799094e510bb348feef53133d12e8cd70c76f24769bfb4aac3a52bb07e262e

Observation 13760481-c44b-4d55-996c-eab131b77628 · outbound

This paper cites write newline.

Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models write newline

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T11:32:21.896732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:32:21.896732Z digest=sha256:555c80ddefa328f44c1814c57a435512c3441dea4cf5feea3cb3e60c21696d9b

Pith citing papers

No inbound Pith citation observations are available.