Paper Citation Record · LEDGER

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2502.03703.

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

pith.paper-citation-record.v1
2502.03703 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:16:22.410668Z

measured 44 of 44 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 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

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation 89676e32-1c06-4a69-a9a6-2488fb13bf4a · outbound

This paper cites write newline.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles write newline

Reference 1

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This paper cites and Lelarge, M.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles and Lelarge, M

Reference 2

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This paper cites A topological characterisation of Weisfeiler-Leman equivalence classes.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles A topological characterisation of Weisfeiler-Leman equivalence classes

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:16:23.082482Z

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This paper cites Equivariant Subgraph Aggregation Networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Equivariant Subgraph Aggregation Networks

Reference 4

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

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles An optimal lower bound on the number of variables for graph identification

Reference 5

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This paper cites On representing linear programs by graph neural networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles On representing linear programs by graph neural networks

Reference 6

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Observation 0e6b0478-7bd1-4ff6-8b9e-b36f07825d02 · outbound

This paper cites W., Jin, W., Rogers, L., Jamison, T.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles W., Jin, W., Rogers, L., Jamison, T

Reference 7

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Observation 9211d598-7bba-45af-8b1a-b29cbff12071 · outbound

This paper cites P., Joshi, C.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles P., Joshi, C

Reference 8

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This paper cites How powerful are k-hop message passing graph neural networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles How powerful are k-hop message passing graph neural networks

Reference 9

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Observation 4bef5d95-cfda-4ad0-ad8c-780d5aec4895 · outbound

This paper cites Understanding and extending subgraph GNN s by rethinking their symmetries.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Understanding and extending subgraph GNN s by rethinking their symmetries

Reference 10

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Observation 4480c658-1156-4755-bc47-4f91e3f52e17 · outbound

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Unresolved cited work

Reference 11

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Observation db7b2287-9f0d-4584-af61-7ff407095b86 · outbound

This paper cites Exact combinatorial optimization with graph convolutional neural networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Exact combinatorial optimization with graph convolutional neural networks

Reference 12

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This paper cites The expressive power of kth-order invariant graph networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles The expressive power of kth-order invariant graph networks

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:16:22.587847Z

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Observation 38cbd877-adb4-4512-89cf-e7532f1126ca · outbound

This paper cites Walk Message Passing Neural Networks and Second-Order Graph Neural Networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Walk Message Passing Neural Networks and Second-Order Graph Neural Networks

Reference 14

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Observation 3f94984e-fe3e-4603-b4eb-85f4a62c0c6c · outbound

This paper cites and Reutter, J.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles and Reutter, J

Reference 15

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Observation 2c67c339-43e2-4fd6-aaeb-0e19c3709c00 · outbound

This paper cites S., Riley, P.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles S., Riley, P

Reference 16

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Observation 7566ff33-cabf-4aa9-b74e-d178fadd7bbc · outbound

This paper cites N., Duvenaud, D., Hern \'a ndez-Lobato, J.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles N., Duvenaud, D., Hern \'a ndez-Lobato, J

Reference 17

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Observation 7d6b73cc-d48b-4488-b0eb-f725436eec7c · outbound

This paper cites L., Ying, R., and Leskovec, J.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles L., Ying, R., and Leskovec, J

Reference 18

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Observation bb99d8b7-ddb2-4418-834a-de3d8087686c · outbound

This paper cites An overview on the application of graph neural networks in wireless networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles An overview on the application of graph neural networks in wireless networks

Reference 19

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This paper cites Boosting the cycle counting power of graph neural networks with I ^2 - GNN s.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Boosting the cycle counting power of graph neural networks with I ^2 - GNN s

Reference 20

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Observation 1766c244-8a36-4308-9f72-ad4f7fc8dc89 · outbound

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

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Semi-Supervised Classification with Graph Convolutional Networks

Reference 21

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Unresolved cited work

Reference 22

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Observation 1ce1a6a3-0eae-4cb7-8f10-3caa1cc5cf2f · outbound

This paper cites R., Wang, Y., and Wang, Y.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles R., Wang, Y., and Wang, Y

Reference 23

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This paper cites Provably powerful graph networks.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Provably powerful graph networks

Reference 24

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles L., Lenssen, J

Reference 25

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This paper cites Weisfeiler and Leman go sparse: T owards scalable higher-order graph embeddings.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Weisfeiler and Leman go sparse: T owards scalable higher-order graph embeddings

Reference 26

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Observation 8a0afef5-c472-48fc-8fcc-b999c58e8ab6 · outbound

This paper cites Graph neural networks for materials science and chemistry.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Graph neural networks for materials science and chemistry

Reference 27

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles C., Hagenbuchner, M., and Monfardini, G

Reference 28

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Graph neural networks in particle physics

Reference 29

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Graph Attention Networks

Reference 30

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Graph attention networks

Reference 31

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles A Review on Graph Neural Network Methods in Financial Applications

Reference 32

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles and Leman, A

Reference 33

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Unresolved cited work

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Unresolved cited work

Reference 35

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles How Powerful are Graph Neural Networks?

Reference 36

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles How powerful are graph neural networks? In International Conference on Learning Representations, 2019

Reference 37

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This paper cites Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34: 0 28877--28888, 2021.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34: 0 28877--28888, 2021

Reference 38

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This paper cites Rethinking the Expressive Power of GNNs via Graph Biconnectivity.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Rethinking the Expressive Power of GNNs via Graph Biconnectivity

Reference 39

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles and Li, P

Reference 40

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This paper cites Graph neural networks and their current applications in bioinformatics.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Graph neural networks and their current applications in bioinformatics

Reference 41

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This paper cites From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

Reference 42

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On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles A practical, progressively-expressive GNN

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This paper cites Graph neural networks: A review of methods and applications.

On the Expressive Power of Subgraph Graph Neural Networks for Graphs with Bounded Cycles Graph neural networks: A review of methods and applications

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:16:22.623424Z

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