Paper Citation Record · LEDGER

On the Effectiveness of Random Weights in Graph Neural Networks

As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2502.00190.

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

pith.paper-citation-record.v1
2502.00190 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:59:58.853813Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T01:51:18.585359Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

44 of 44 outbound references displayed

verified exact2
verified fuzzy29
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 7a79f183-3a81-4284-90a9-c074d8f96152 · outbound

This paper cites The surprising power of graph neural networks with random node initialization.

On the Effectiveness of Random Weights in Graph Neural Networks The surprising power of graph neural networks with random node initialization

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.315327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 86ca6083-f428-4dc1-9371-69b36a063700 · outbound

This paper cites Discrete and Continuous Deep Residual Learning Over Graphs.

On the Effectiveness of Random Weights in Graph Neural Networks Discrete and Continuous Deep Residual Learning Over Graphs

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:59:58.996202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af6240be-2018-4521-a9eb-1313c4d577ea · outbound

This paper cites Pyramidal reservoir graph neural network.

On the Effectiveness of Random Weights in Graph Neural Networks Pyramidal reservoir graph neural network

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.304501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ba36588-6caa-4354-a8c2-f561c00e0612 · outbound

This paper cites Experiment tracking with weights and biases, 2020.

On the Effectiveness of Random Weights in Graph Neural Networks Experiment tracking with weights and biases, 2020

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.708666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e73a6345-b279-4f9c-a546-5ed892f694cd · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

On the Effectiveness of Random Weights in Graph Neural Networks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.712439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 294d53e5-b74a-4d54-98a2-42ae1d3ccf5b · outbound

This paper cites A unified lottery ticket hypothesis for graph neural networks.

On the Effectiveness of Random Weights in Graph Neural Networks A unified lottery ticket hypothesis for graph neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.287397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 867f44b3-a207-49ad-bb8c-f2a2df12147f · outbound

This paper cites Pruning randomly initialized neural networks with iterative randomization.

On the Effectiveness of Random Weights in Graph Neural Networks Pruning randomly initialized neural networks with iterative randomization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.277190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a212a9e9-5edc-44eb-afb9-cbe0115337ec · outbound

This paper cites Investigating over-parameterized randomized graph networks.

On the Effectiveness of Random Weights in Graph Neural Networks Investigating over-parameterized randomized graph networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.267102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57429d7e-0e4f-4f7e-95d9-3534e77c204e · outbound

This paper cites Benchmarking graph neural networks.

On the Effectiveness of Random Weights in Graph Neural Networks Benchmarking graph neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.257064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 28338288-f7df-49a1-aad2-6b5a27e0e8be · outbound

This paper cites Graph positional encoding via random feature propagation.

On the Effectiveness of Random Weights in Graph Neural Networks Graph positional encoding via random feature propagation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.246670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f31fa2bf-ec4b-4908-afa6-4e44d18b84fd · outbound

This paper cites Improving graph neural networks with learnable propagation operators.

On the Effectiveness of Random Weights in Graph Neural Networks Improving graph neural networks with learnable propagation operators

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.236607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed0fea6f-8e30-46bb-a594-07b525668538 · outbound

This paper cites GRANOLA: Adaptive Normalization for Graph Neural Networks.

On the Effectiveness of Random Weights in Graph Neural Networks GRANOLA: Adaptive Normalization for Graph Neural Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.738401Z

Source-reported events for the cited work

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Observation 7b10a3da-905b-4549-994e-f2a38b5591d5 · outbound

This paper cites Graph random neural networks for semi-supervised learning on graphs.

On the Effectiveness of Random Weights in Graph Neural Networks Graph random neural networks for semi-supervised learning on graphs

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.226127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 715c207b-85c4-4bdf-95e6-1708414833e8 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

On the Effectiveness of Random Weights in Graph Neural Networks Fast Graph Representation Learning with PyTorch Geometric

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.745401Z

Source-reported events for the cited work

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Observation 4a634b98-f0c8-450c-8a5d-e25da65bad62 · outbound

This paper cites Graph echo state networks.

On the Effectiveness of Random Weights in Graph Neural Networks Graph echo state networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.215639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d2218f5-99a7-4f74-8b7d-c32dadb8995b · outbound

This paper cites Fast and deep graph neural networks.

On the Effectiveness of Random Weights in Graph Neural Networks Fast and deep graph neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.205820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0abed0be-01cf-486c-9196-95de17f8481d · outbound

This paper cites Extreme learning machine to graph convolutional networks.

On the Effectiveness of Random Weights in Graph Neural Networks Extreme learning machine to graph convolutional networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.196434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cb6a8170-71bd-4d5b-884b-f9c086e5d503 · outbound

This paper cites Inductive representation learning on large graphs.

On the Effectiveness of Random Weights in Graph Neural Networks Inductive representation learning on large graphs

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.186618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e925a93-dc90-49fd-904e-0cbe667515bb · outbound

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

On the Effectiveness of Random Weights in Graph Neural Networks Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.762819Z

Source-reported events for the cited work

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Observation 600894d1-a265-4505-b315-93dc707ad822 · outbound

This paper cites Extreme learning machine: theory and applications.

On the Effectiveness of Random Weights in Graph Neural Networks Extreme learning machine: theory and applications

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.176804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7291b25-3df0-4da8-8c6f-8354fecd3b78 · outbound

This paper cites Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data.

On the Effectiveness of Random Weights in Graph Neural Networks Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.770254Z

Source-reported events for the cited work

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Observation f207c4f9-8fe5-4b30-b373-f86c64632e63 · outbound

This paper cites You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets.

On the Effectiveness of Random Weights in Graph Neural Networks You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:59:58.935741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0ab9728-c40e-4088-8647-77bf65857018 · outbound

This paper cites echo state.

On the Effectiveness of Random Weights in Graph Neural Networks echo state

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.166993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8944d98-6d8a-4b42-8412-3c9cc3e1f241 · outbound

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

On the Effectiveness of Random Weights in Graph Neural Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.783295Z

Source-reported events for the cited work

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Observation cf86e3b8-a4b3-417f-8dad-2314b2638a39 · outbound

This paper cites Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification.

On the Effectiveness of Random Weights in Graph Neural Networks Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.786836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 227eb5fa-1174-4bb9-8071-462cb5d5e6b3 · outbound

This paper cites Automating the construction of internet portals with machine learning.

On the Effectiveness of Random Weights in Graph Neural Networks Automating the construction of internet portals with machine learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.157261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42102180-5e45-41e2-8913-a2640b7ba5c3 · outbound

This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

On the Effectiveness of Random Weights in Graph Neural Networks TUDataset: A collection of benchmark datasets for learning with graphs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.794006Z

Source-reported events for the cited work

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Observation b6b01fae-973d-475b-92be-ccae1aadb7e1 · outbound

This paper cites Relational pooling for graph representations.

On the Effectiveness of Random Weights in Graph Neural Networks Relational pooling for graph representations

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.146738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8daf1f1e-aab9-45b2-82ac-e91868b807dc · outbound

This paper cites Query-driven active surveying for collective classification.

On the Effectiveness of Random Weights in Graph Neural Networks Query-driven active surveying for collective classification

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.135637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa54ae5f-c6eb-44d4-ba88-b7591f7e74e4 · outbound

This paper cites An untrained neural model for fast and accurate graph classification.

On the Effectiveness of Random Weights in Graph Neural Networks An untrained neural model for fast and accurate graph classification

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.124901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bb4339bc-cbe2-4b75-8f2a-3bf7c9923f6f · outbound

This paper cites Multiresolution reservoir graph neural network.

On the Effectiveness of Random Weights in Graph Neural Networks Multiresolution reservoir graph neural network

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.114343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a59e5ec-6a7d-49a0-ba34-c9d481c09c2f · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

On the Effectiveness of Random Weights in Graph Neural Networks Pytorch: An imperative style, high-performance deep learning library

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.103522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5aacce06-ce6b-4c57-84c6-fb6b05cae555 · outbound

This paper cites Global Attention Improves Graph Networks Generalization.

On the Effectiveness of Random Weights in Graph Neural Networks Global Attention Improves Graph Networks Generalization

Reference 33

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Source-reported events for the cited work

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Observation 13de7bcb-6bee-4661-96c1-74cdf091a50d · outbound

This paper cites What's hidden in a randomly weighted neural network? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020.

On the Effectiveness of Random Weights in Graph Neural Networks What's hidden in a randomly weighted neural network? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.092566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac6887d6-c42b-4e21-9ab8-d8d505c3d896 · outbound

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

On the Effectiveness of Random Weights in Graph Neural Networks Rank collapse causes over-smoothing and over-correlation in graph neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.081579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d8ee766-c78b-4903-a014-094e8c1cf41b · outbound

This paper cites Random features strengthen graph neural networks.

On the Effectiveness of Random Weights in Graph Neural Networks Random features strengthen graph neural networks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.071329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7db2e4f8-8db2-45a6-95ca-7b39c9586d9b · outbound

This paper cites Collective classification in network data.

On the Effectiveness of Random Weights in Graph Neural Networks Collective classification in network data

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.060896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4175be1-aecd-4562-aa53-b7d760ae41ec · outbound

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

On the Effectiveness of Random Weights in Graph Neural Networks Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.833705Z

Source-reported events for the cited work

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Observation 8d463b38-f3c4-4ce6-a5c9-773c19919e8f · outbound

This paper cites Searching lottery tickets in graph neural networks: A dual perspective.

On the Effectiveness of Random Weights in Graph Neural Networks Searching lottery tickets in graph neural networks: A dual perspective

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.049613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 34234e84-b5a3-4ee1-93c3-49dbd5bbcf14 · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations , 2019.

On the Effectiveness of Random Weights in Graph Neural Networks How powerful are graph neural networks? In International Conference on Learning Representations , 2019

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.840418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1246b6d6-bf0a-4c5d-b30f-7f4ff97ed534 · outbound

This paper cites Are graph augmentations necessary? simple graph contrastive learning for recommendation.

On the Effectiveness of Random Weights in Graph Neural Networks Are graph augmentations necessary? simple graph contrastive learning for recommendation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.033274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 528ed563-2f17-4057-9c52-ce6cab5c9dae · outbound

This paper cites Graph convolutional extreme learning machine.

On the Effectiveness of Random Weights in Graph Neural Networks Graph convolutional extreme learning machine

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.022663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6aa65de3-86a9-4dce-9850-6273a2ba1866 · outbound

This paper cites Semi-supervised learning with graph convolutional extreme learning machines.

On the Effectiveness of Random Weights in Graph Neural Networks Semi-supervised learning with graph convolutional extreme learning machines

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:59:59.012770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0809148b-1506-4207-b415-67e4930819e9 · outbound

This paper cites write newline.

On the Effectiveness of Random Weights in Graph Neural Networks write newline

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.853813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation ba030028-90c7-4b74-bc4f-f6723d7bc1cf · inbound

Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation cites this paper.

Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation On the Effectiveness of Random Weights in Graph Neural Networks

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:01:03.676421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 98fe3903-ca0e-4008-a250-b97f32ad9c47 · inbound

Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models cites this paper.

Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models On the Effectiveness of Random Weights in Graph Neural Networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-27T02:00:22.152521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.