Paper Citation Record · LEDGER

Bridging Theory and Practice in Link Representation with Graph Neural Networks

As of 7 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2506.24018.

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

pith.paper-citation-record.v1
2506.24018 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

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measured 65 of 65 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

64 of 64 outbound references displayed

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External citation measurements

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Outbound references

Observation 5080a19b-6f90-4734-a590-67b45fed7b1b · outbound

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

Bridging Theory and Practice in Link Representation with Graph Neural Networks The Surprising Power of Graph Neural Networks with Random Node Initialization

Reference 1

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Observation 597f046c-2dba-41e8-b6e0-7a004bf8e202 · outbound

This paper cites Breaking the limits of message passing graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Breaking the limits of message passing graph neural networks

Reference 2

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Observation 8d9be890-c5c0-4332-a72f-988dd1e37932 · outbound

This paper cites How symmetric are real-world graphs? a large-scale study.

Bridging Theory and Practice in Link Representation with Graph Neural Networks How symmetric are real-world graphs? a large-scale study

Reference 3

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This paper cites Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Reference 4

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This paper cites Bronstein, and Haggai Maron.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Bronstein, and Haggai Maron

Reference 5

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This paper cites The expressive power of pooling in graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks The expressive power of pooling in graph neural networks

Reference 6

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This paper cites Improving graph neural network expressivity via subgraph isomorphism counting.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Improving graph neural network expressivity via subgraph isomorphism counting

Reference 7

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This paper cites siamese.

Bridging Theory and Practice in Link Representation with Graph Neural Networks siamese

Reference 8

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This paper cites A Note on Over-Smoothing for Graph Neural Networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks A Note on Over-Smoothing for Graph Neural Networks

Reference 9

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This paper cites An optimal lower bound on the number of variables for graph identification.

Bridging Theory and Practice in Link Representation with Graph Neural Networks An optimal lower bound on the number of variables for graph identification

Reference 10

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Observation 898655a2-f426-4d8e-96d2-e66627ac8c59 · outbound

This paper cites Line graph neural networks for link prediction.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Line graph neural networks for link prediction

Reference 11

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Observation adf4b6f9-8094-42f7-b5cc-5bb45acd9880 · outbound

This paper cites Bronstein, and Max Hansmire.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Bronstein, and Max Hansmire

Reference 12

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This paper cites Edge classification on graphs: New directions in topological imbalance.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Edge classification on graphs: New directions in topological imbalance

Reference 13

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This paper cites Generalizations of k-dimensional weisfeiler–leman stabiliza- tion.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Generalizations of k-dimensional weisfeiler–leman stabiliza- tion

Reference 14

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Observation af94712f-a917-4368-b078-023b0e57a4f2 · outbound

This paper cites The link regression problem in graph streams.

Bridging Theory and Practice in Link Representation with Graph Neural Networks The link regression problem in graph streams

Reference 15

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Observation 743a3aaa-d506-4690-8787-17d351d3fc2f · outbound

This paper cites A Fair Comparison of Graph Neural Networks for Graph Classification.

Bridging Theory and Practice in Link Representation with Graph Neural Networks A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 16

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Observation 40dc075a-712c-4e98-abee-7d14adb285f0 · outbound

This paper cites Meta-path learning for multi-relational graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Meta-path learning for multi-relational graph neural networks

Reference 17

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Unresolved cited work

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This paper cites The iteration number of the weisfeiler-leman algorithm.

Bridging Theory and Practice in Link Representation with Graph Neural Networks The iteration number of the weisfeiler-leman algorithm

Reference 19

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This paper cites Inductive representation learning on large graphs.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Inductive representation learning on large graphs

Reference 20

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This paper cites The generalization of student’s ratio.

Bridging Theory and Practice in Link Representation with Graph Neural Networks The generalization of student’s ratio

Reference 21

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Bridging Theory and Practice in Link Representation with Graph Neural Networks The $k$-Dimensional Weisfeiler-Leman Algorithm

Reference 22

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This paper cites Prediction of protein–protein interaction using graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Prediction of protein–protein interaction using graph neural networks

Reference 23

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This paper cites Is expressivity essential for the predictive perfor- mance of graph neural networks? In NeurIPS 2024 Workshop on Scientific Methods for Under- standing Deep Learning, 2024.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Is expressivity essential for the predictive perfor- mance of graph neural networks? In NeurIPS 2024 Workshop on Scientific Methods for Under- standing Deep Learning, 2024

Reference 24

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Observation 68df4e3d-5707-4afc-b17a-081fc9867df6 · outbound

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

Bridging Theory and Practice in Link Representation with Graph Neural Networks Semi-Supervised Classification with Graph Convolutional Networks

Reference 25

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Variational Graph Auto-Encoders

Reference 26

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Kipf and Max Welling

Reference 27

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Observation 8d13fd6c-d811-49f8-ab74-656d5dd78ecc · outbound

This paper cites A simple and expressive graph neural network based method for structural link representation.

Bridging Theory and Practice in Link Representation with Graph Neural Networks A simple and expressive graph neural network based method for structural link representation

Reference 28

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This paper cites Evaluating graph neural networks for link prediction: Current pitfalls and new bench- marking.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Evaluating graph neural networks for link prediction: Current pitfalls and new bench- marking

Reference 29

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Observation a1dfd6ca-fa27-4a18-93e9-ab0ae6649fcc · outbound

This paper cites Line graph neural networks for link weight prediction.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Line graph neural networks for link weight prediction

Reference 30

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Observation 840e0efd-590f-40a8-900f-63ea0a9c0935 · outbound

This paper cites Computational complexity of the weisfeiler-leman dimension.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Computational complexity of the weisfeiler-leman dimension

Reference 31

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This paper cites Link prediction in complex networks: A survey.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Link prediction in complex networks: A survey

Reference 32

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Simplifying approach to node classifica- tion in graph neural networks

Reference 33

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Weisfeiler and leman go neural: Higher-order graph neural networks

Reference 34

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This paper cites Position: Future directions in the theory of graph machine learning.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Position: Future directions in the theory of graph machine learning

Reference 35

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This paper cites Orbit-equivariant graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Orbit-equivariant graph neural networks

Reference 36

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Relational pooling for graph representations

Reference 37

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This paper cites A review of relational machine learning for knowledge graphs.

Bridging Theory and Practice in Link Representation with Graph Neural Networks A review of relational machine learning for knowledge graphs

Reference 38

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This paper cites Knowledge graph embedding for link prediction: A comparative analysis.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Knowledge graph embedding for link prediction: A comparative analysis

Reference 39

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This paper cites On the equivalence between positional node embeddings and structural graph representations.

Bridging Theory and Practice in Link Representation with Graph Neural Networks On the equivalence between positional node embeddings and structural graph representations

Reference 40

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Graph attention networks

Reference 41

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This paper cites Neural common neighbor with completion for link prediction.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Neural common neighbor with completion for link prediction

Reference 42

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This paper cites Apan: Asynchronous propagation attention network for real-time temporal graph embedding.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Apan: Asynchronous propagation attention network for real-time temporal graph embedding

Reference 43

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This paper cites An Empirical Study of Realized GNN Expressiveness.

Bridging Theory and Practice in Link Representation with Graph Neural Networks An Empirical Study of Realized GNN Expressiveness

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This paper cites An empirical study of realized GNN expressiveness.

Bridging Theory and Practice in Link Representation with Graph Neural Networks An empirical study of realized GNN expressiveness

Reference 45

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Bridging Theory and Practice in Link Representation with Graph Neural Networks The reduction of a graph to canonical form and the algebra which appears therein

Reference 46

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Graph neural networks in node classification: survey and evaluation

Reference 47

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Active and semi-supervised graph neural networks for graph classification

Reference 48

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Observation d957cd1c-1b40-4e77-9b90-4f5b30f51994 · outbound

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

Bridging Theory and Practice in Link Representation with Graph Neural Networks How powerful are graph neural networks? In International Conference on Learning Representations , 2019

Reference 49

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This paper cites How powerful are graph neural networks?, 2019.

Bridging Theory and Practice in Link Representation with Graph Neural Networks How powerful are graph neural networks?, 2019

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.465524Z

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Observation 72284625-aa62-4e1b-8661-289709bb09ec · outbound

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Bridging Theory and Practice in Link Representation with Graph Neural Networks Graph convolutional neural networks for web-scale recommender systems

Reference 51

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This paper cites Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction

Reference 52

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This paper cites Link prediction based on graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Link prediction based on graph neural networks

Reference 53

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This paper cites Labeling trick: A theory of using graph neural networks for multi-node representation learning.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Labeling trick: A theory of using graph neural networks for multi-node representation learning

Reference 54

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Unavailable: canonical work link unavailable.

Observation ffb12248-a0bc-4bea-9bb9-36049cc97df3 · outbound

This paper cites From relational pooling to subgraph gnns: A universal framework for more expressive graph neural networks.

Bridging Theory and Practice in Link Representation with Graph Neural Networks From relational pooling to subgraph gnns: A universal framework for more expressive graph neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.413226Z

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.

Observation e3fa873a-8c67-4b8a-a161-a43b9cb2e8af · outbound

This paper cites Progresses and challenges in link prediction.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Progresses and challenges in link prediction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.399795Z

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.

Observation 998e1cfd-49cd-4b40-bd4c-c58aae64ab4a · outbound

This paper cites If mM = 0, then, regardless of kM ϕ , M is not able to distinguish between links whose endpoints are automorphic, i.e., ∀F ∈ M, ∀(u, v), (u′, v′) s.t.

Bridging Theory and Practice in Link Representation with Graph Neural Networks If mM = 0, then, regardless of kM ϕ , M is not able to distinguish between links whose endpoints are automorphic, i.e., ∀F ∈ M, ∀(u, v), (u′, v′) s.t

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.386415Z

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.

Observation 6a5b75c0-f551-4100-9cc2-716d1bdb8410 · outbound

This paper cites If mM1 + lM1 ≤ mM2 + lM2, then M1 ≤ M2.

Bridging Theory and Practice in Link Representation with Graph Neural Networks If mM1 + lM1 ≤ mM2 + lM2, then M1 ≤ M2

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.372169Z

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.

Observation a567187d-4d6a-4833-8a4a-31ce342d6b53 · outbound

This paper cites If kM1 ρ ≤ kM2 ρ , then M1 ≤ M2.

Bridging Theory and Practice in Link Representation with Graph Neural Networks If kM1 ρ ≤ kM2 ρ , then M1 ≤ M2

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.357281Z

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.

Observation c16719cc-da16-4506-ad2b-8f458014359c · outbound

This paper cites an unresolved cited work.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:35:08.343997Z

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.

Observation f22a8dfd-5854-41d1-8acd-effa3bd0d848 · outbound

This paper cites an unresolved cited work.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:35:08.330436Z

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.

Observation 36786536-2b1c-4b89-806d-bf862a7ed87d · outbound

This paper cites NCN uses a fixed radius mNCN = 1 (Table 1), but allows a configurable number of layers.

Bridging Theory and Practice in Link Representation with Graph Neural Networks NCN uses a fixed radius mNCN = 1 (Table 1), but allows a configurable number of layers

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:35:08.316956Z

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.

Observation 4bfe08f2-084a-42da-a58f-b6d938fe6cb4 · outbound

This paper cites an unresolved cited work.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Unresolved cited work

Reference 64

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:35:08.302092Z

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.

Observation 48864275-afef-4760-969a-4741aeb1f986 · outbound

This paper cites an unresolved cited work.

Bridging Theory and Practice in Link Representation with Graph Neural Networks Unresolved cited work

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T21:35:07.984844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c4e22aaa-250f-4c6c-8d36-ff7907fd075a · inbound

Plain Transformers are Surprisingly Powerful Link Predictors cites this paper.

Plain Transformers are Surprisingly Powerful Link Predictors Bridging Theory and Practice in Link Representation with Graph Neural Networks

Reference 2017

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:44:24.926127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.