Paper Citation Record · LEDGER

What Can We Learn From MIMO Graph Convolutions?

As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.11346.

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

pith.paper-citation-record.v1
2505.11346 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:04:08.203440Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

45 of 45 outbound references displayed

verified exact0
verified fuzzy37
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02d7169d-3aed-4d68-b38f-184763a2873d · outbound

This paper cites Beyond low-frequency information in graph convolutional networks.

What Can We Learn From MIMO Graph Convolutions? Beyond low-frequency information in graph convolutional networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:09.057985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b7777fbb-7792-4e85-bb5e-ef39914a7197 · outbound

This paper cites Spectral networks and locally connected networks on graphs.

What Can We Learn From MIMO Graph Convolutions? Spectral networks and locally connected networks on graphs

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:09.007016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b4376f82-8f6d-4674-8a25-a137177bbc53 · outbound

This paper cites Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson.

What Can We Learn From MIMO Graph Convolutions? Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.934667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 18148faf-fd0f-4f7a-814b-06bf6b1a1786 · outbound

This paper cites Feature Transportation Improves Graph Neural Networks.

What Can We Learn From MIMO Graph Convolutions? Feature Transportation Improves Graph Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:04:08.011950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af811dc0-4cb8-45b7-bd8d-3a5924a43f32 · outbound

This paper cites On random graphs i.

What Can We Learn From MIMO Graph Convolutions? On random graphs i

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.916726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cdebd697-f6c4-49ee-b2f7-04a3572a3790 · outbound

This paper cites OGB- LSC: A large-scale challenge for machine learning on graphs.

What Can We Learn From MIMO Graph Convolutions? OGB- LSC: A large-scale challenge for machine learning on graphs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.845051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5d1c185d-3183-4c04-8a7c-6278fa637e9d · outbound

This paper cites Aggarwal, and Jiliang Tang.

What Can We Learn From MIMO Graph Convolutions? Aggarwal, and Jiliang Tang

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.828410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d0d7eea0-f73c-467b-bf89-3f15f044db93 · outbound

This paper cites Kipf and Max Welling.

What Can We Learn From MIMO Graph Convolutions? Kipf and Max Welling

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.812651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4d1c7a72-4e31-47cf-956f-d1784dd1facb · outbound

This paper cites A reduction of a graph to a canonical form and an algebra arising during this reduction.

What Can We Learn From MIMO Graph Convolutions? A reduction of a graph to a canonical form and an algebra arising during this reduction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.763986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 268df92c-8f7a-465a-be77-e2ed99f8a5fe · outbound

This paper cites Graph neural networks exponentially lose expressive power for node classification.

What Can We Learn From MIMO Graph Convolutions? Graph neural networks exponentially lose expressive power for node classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.696042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b4931ae-258e-46b3-904a-db3a35a574a2 · outbound

This paper cites Convolution operators and L(p,q ) spaces.

What Can We Learn From MIMO Graph Convolutions? Convolution operators and L(p,q ) spaces

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.679433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8b36a260-8b5b-46b4-b5da-64f921ca2655 · outbound

This paper cites Recipe for a general, pow- erful, scalable graph transformer.

What Can We Learn From MIMO Graph Convolutions? Recipe for a general, pow- erful, scalable graph transformer

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.647488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4c5ea199-06fe-43b8-8c9a-582db1c8b64c · outbound

This paper cites Transforming pagerank into an infinite-depth graph neural network.

What Can We Learn From MIMO Graph Convolutions? Transforming pagerank into an infinite-depth graph neural network

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.632198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94fd7fcc-b925-4622-ab6f-211e5753406a · outbound

This paper cites Rank collapse causes over-smoothing and over-correlation in graph neural networks.

What Can We Learn From MIMO Graph Convolutions? Rank collapse causes over-smoothing and over-correlation in graph neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.615623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 747b7770-496d-4ca4-89a5-1238aef30cc7 · outbound

This paper cites Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph.

What Can We Learn From MIMO Graph Convolutions? Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T21:04:08.127821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e30b65a-abee-4544-af6c-5ed0d2f716d8 · outbound

This paper cites Konstantin Rusch, Benjamin Paul Chamberlain, Michael W.

What Can We Learn From MIMO Graph Convolutions? Konstantin Rusch, Benjamin Paul Chamberlain, Michael W

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.599105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bbd48874-0332-4ab1-bba6-9e42190373e1 · outbound

This paper cites Discrete signal processing on graphs.

What Can We Learn From MIMO Graph Convolutions? Discrete signal processing on graphs

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.583316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb5388bd-8ef1-4232-be69-2d5d6e012cfc · outbound

This paper cites an unresolved cited work.

What Can We Learn From MIMO Graph Convolutions? Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:04:08.567514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9872d478-c6a3-482f-86e0-19b684a03ac1 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

What Can We Learn From MIMO Graph Convolutions? Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.535165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 93631824-ddae-4077-81ec-553488e54aaa · outbound

This paper cites Graph attention networks.

What Can We Learn From MIMO Graph Convolutions? Graph attention networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.518341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 07732558-01d9-496c-8bc2-5377410a2495 · outbound

This paper cites Replacing softmax with ReLU in Vision Transformers.

What Can We Learn From MIMO Graph Convolutions? Replacing softmax with ReLU in Vision Transformers

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T21:04:08.172998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82a4f8a2-9d9d-4ee7-97a6-b23b77aa236e · outbound

This paper cites Representation learning on graphs with jump- ing knowledge networks.

What Can We Learn From MIMO Graph Convolutions? Representation learning on graphs with jump- ing knowledge networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.501401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 448f3c75-2252-4352-8720-412251131c2b · outbound

This paper cites How powerful are graph neural net- works? In 7th International Conference on Learning Rep- resentations, New Orleans, LA, USA, May 6-9,.

What Can We Learn From MIMO Graph Convolutions? How powerful are graph neural net- works? In 7th International Conference on Learning Rep- resentations, New Orleans, LA, USA, May 6-9,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.484274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 16cd298b-c115-4c97-ba97-910ddb670272 · outbound

This paper cites Two sides of the same coin: Heterophily and oversmoothing in graph con- volutional neural networks.

What Can We Learn From MIMO Graph Convolutions? Two sides of the same coin: Heterophily and oversmoothing in graph con- volutional neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.466079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bd6012d9-5650-4cb3-bf92-39e1d4509607 · outbound

This paper cites Multi-channel graph neural networks.

What Can We Learn From MIMO Graph Convolutions? Multi-channel graph neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.450328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e48b7c24-bedf-4603-b082-3f93065b4046 · outbound

This paper cites We use the vectorized signal ˆx = vec(X) ∈ Rn·c by stacking its columns.

What Can We Learn From MIMO Graph Convolutions? We use the vectorized signal ˆx = vec(X) ∈ Rn·c by stacking its columns

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.433293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 857332fb-0788-40e9-a681-2d8f15ebe37c · outbound

This paper cites an unresolved cited work.

What Can We Learn From MIMO Graph Convolutions? Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:04:08.412222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5c0ba3f4-fbc8-4a98-a4bc-ed7cb9cbb77b · outbound

This paper cites Marques, Alejandro Ribeiro, and Geert Leus.

What Can We Learn From MIMO Graph Convolutions? Marques, Alejandro Ribeiro, and Geert Leus

Reference 1959

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.895608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3de57ead-590d-448a-9876-b37fcc8dc97b · outbound

This paper cites Geom-gcn: Geo- metric graph convolutional networks.

What Can We Learn From MIMO Graph Convolutions? Geom-gcn: Geo- metric graph convolutional networks

Reference 1963

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.663355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 57c50634-ebe0-4dfc-a673-0c87b4fb63d4 · outbound

This paper cites Bronstein.

What Can We Learn From MIMO Graph Convolutions? Bronstein

Reference 1968

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.747966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 916e1bf0-fb4a-440b-9d5d-91912c0a1eae · outbound

This paper cites Hamilton, Vincent L´etourneau, and Prudencio Tossou.

What Can We Learn From MIMO Graph Convolutions? Hamilton, Vincent L´etourneau, and Prudencio Tossou

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.780310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bd2f2607-b62e-4b84-9241-aedcf3d0b16d · outbound

This paper cites Sheaf Neural Networks.

What Can We Learn From MIMO Graph Convolutions? Sheaf Neural Networks

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-15T21:04:08.034748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 134e10a1-3cf3-4553-8fee-2d50a3df9c04 · outbound

This paper cites Rethinking softmax: Self-attention with polynomial acti- vations.

What Can We Learn From MIMO Graph Convolutions? Rethinking softmax: Self-attention with polynomial acti- vations

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-15T21:04:08.148201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1599d9db-da10-4365-bd1d-9410b50f2465 · outbound

This paper cites Convolutional learning on multigraphs.

What Can We Learn From MIMO Graph Convolutions? Convolutional learning on multigraphs

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.990317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f15c0c40-e98e-4b41-b6c0-ae73c33e72bb · outbound

This paper cites Where did the gap go? re- assessing the long-range graph benchmark.

What Can We Learn From MIMO Graph Convolutions? Where did the gap go? re- assessing the long-range graph benchmark

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.551405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aed620b9-0bec-43e6-a495-6ada96a88b53 · outbound

This paper cites Long range graph benchmark.

What Can We Learn From MIMO Graph Convolutions? Long range graph benchmark

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.955977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 93be8abe-2d01-4db3-9c42-bd4941a5b93c · outbound

This paper cites Kolda and Brett W.

What Can We Learn From MIMO Graph Convolutions? Kolda and Brett W

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.796529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 45551539-5fda-4b63-9067-3466210d3dc5 · outbound

This paper cites Wavelets on graphs via spectral graph theory.

What Can We Learn From MIMO Graph Convolutions? Wavelets on graphs via spectral graph theory

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.878215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e020ce8b-12d5-4ccf-b4cb-c4a5c502bf61 · outbound

This paper cites Gpnet: Simpli- fying graph neural networks via multi-channel geometric polynomials.

What Can We Learn From MIMO Graph Convolutions? Gpnet: Simpli- fying graph neural networks via multi-channel geometric polynomials

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.731629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a8e70092-af58-40eb-bed5-a9c8bbc15928 · outbound

This paper cites Deep residual learning for image recog- nition.

What Can We Learn From MIMO Graph Convolutions? Deep residual learning for image recog- nition

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.861909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9a4ab36b-d669-498b-9881-751ef7359ba9 · outbound

This paper cites Bronstein.

What Can We Learn From MIMO Graph Convolutions? Bronstein

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:09.040668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e2f55f5-4f89-47b5-9d86-44f4bdd029bd · outbound

This paper cites How attentive are graph attention networks? In The Tenth International Conference on Learning Representa- tions, Virtual Event, April 25-29,.

What Can We Learn From MIMO Graph Convolutions? How attentive are graph attention networks? In The Tenth International Conference on Learning Representa- tions, Virtual Event, April 25-29,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:09.023535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3ffc760f-63b6-416a-9a03-5daae847e3c8 · outbound

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

What Can We Learn From MIMO Graph Convolutions? Convolutional neural networks on graphs with fast localized spectral filtering

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.973770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5eb57617-2398-43f1-be36-2c033bc4bd08 · outbound

This paper cites Simplifying the Theory on Over-Smoothing.

What Can We Learn From MIMO Graph Convolutions? Simplifying the Theory on Over-Smoothing

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T21:04:08.132753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 131da6d1-b59a-4162-ad56-01ea9e9d7f85 · outbound

This paper cites Revisiting heterophily for graph neural networks.

What Can We Learn From MIMO Graph Convolutions? Revisiting heterophily for graph neural networks

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:04:08.714677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.