Paper Citation Record · LEDGER

Universality and Approximation Rates of Graph Neural Networks with Random Features

As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.26699.

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

pith.paper-citation-record.v1
2607.26699 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T23:40:06.777696Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

16 of 16 outbound references displayed

verified exact1
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fad5cb2e-c2b2-4873-958a-adb17d2b68b0 · outbound

This paper cites The Surprising Power of Graph Neural Networks with Random Node Initialization.

Universality and Approximation Rates of Graph Neural Networks with Random Features The Surprising Power of Graph Neural Networks with Random Node Initialization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.655042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80d52464-2eca-4060-a2c3-b1cfda5af7b8 · outbound

This paper cites What graph neural networks cannot learn: depth vs width.

Universality and Approximation Rates of Graph Neural Networks with Random Features What graph neural networks cannot learn: depth vs width

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.731824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f97761bf-e5e1-4ba3-a628-87d054c84277 · outbound

This paper cites Global Attention Improves Graph Networks Generalization.

Universality and Approximation Rates of Graph Neural Networks with Random Features Global Attention Improves Graph Networks Generalization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.752628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 938c4961-6e6a-4cd7-914f-7f0c4bf67468 · outbound

This paper cites Graph Attention Networks.

Universality and Approximation Rates of Graph Neural Networks with Random Features Graph Attention Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.765668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba7cf4c4-aca5-4265-9e1b-5fd7baec3100 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Universality and Approximation Rates of Graph Neural Networks with Random Features How Powerful are Graph Neural Networks?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.777696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9428d49-cefd-440e-a990-3990de99cdce · outbound

This paper cites Coloring graph neural networks for node disambiguation.

Universality and Approximation Rates of Graph Neural Networks with Random Features Coloring graph neural networks for node disambiguation

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.694972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51598fdb-860a-41b9-9442-9ffeec890764 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Universality and Approximation Rates of Graph Neural Networks with Random Features Relational inductive biases, deep learning, and graph networks

Reference 1993

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.668285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7870e97-ceab-451e-a25b-8dbda76043fe · outbound

This paper cites Graph Positional and Structural Encoder.

Universality and Approximation Rates of Graph Neural Networks with Random Features Graph Positional and Structural Encoder

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.686596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc5d4908-510e-4914-a30b-083faefa7b64 · outbound

This paper cites Invariant and Equivariant Graph Networks.

Universality and Approximation Rates of Graph Neural Networks with Random Features Invariant and Equivariant Graph Networks

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.742866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd9dc4e1-0ba7-4473-8924-c1cc0795200d · outbound

This paper cites A Review on Graph Neural Network Methods in Financial Applications.

Universality and Approximation Rates of Graph Neural Networks with Random Features A Review on Graph Neural Network Methods in Financial Applications

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.770996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 529f5821-e9e8-4981-847d-1b82695dac18 · outbound

This paper cites On the Utilization of Unique Node Identifiers in Graph Neural Networks.

Universality and Approximation Rates of Graph Neural Networks with Random Features On the Utilization of Unique Node Identifiers in Graph Neural Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.676120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be7718d9-3a0d-4e85-826b-62fdaf75d1e6 · outbound

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

Universality and Approximation Rates of Graph Neural Networks with Random Features Semi-Supervised Classification with Graph Convolutional Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.723577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f473e5e-55c4-4e90-8cbd-39e22128f4a7 · outbound

This paper cites The logical expressiveness of graph neural networks.

Universality and Approximation Rates of Graph Neural Networks with Random Features The logical expressiveness of graph neural networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.662166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe554a1c-d427-4151-a318-d12451d5d681 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Universality and Approximation Rates of Graph Neural Networks with Random Features Fast Graph Representation Learning with PyTorch Geometric

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.702900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b83af420-afc2-48fc-9781-4317fbb9c94a · outbound

This paper cites Random features strengthen graph neural networks.

Universality and Approximation Rates of Graph Neural Networks with Random Features Random features strengthen graph neural networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-07-30T23:40:06.759276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9462294-fdeb-4740-bfce-d41015bfdc68 · outbound

This paper cites URLhttps://doi.org/10.1137/24M1697402.

Universality and Approximation Rates of Graph Neural Networks with Random Features URLhttps://doi.org/10.1137/24M1697402

Reference 2026

Resolution: verified exact; doi, observed 2026-07-30T23:40:57.546583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.