Paper Citation Record · LEDGER

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

As of 18 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 2 inbound Pith citation observations for arXiv:2501.03113.

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

pith.paper-citation-record.v1
2501.03113 v2

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:02:05.477635Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:57:11.727855Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T10:21:30.278482Z

Reference resolution

79 of 79 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 41be9554-be2f-416f-a8e2-a50108b756ec · outbound

This paper cites write newline.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality write newline

Reference 1

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This paper cites \.I ., Grohe, M., and Lukasiewicz, T.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality \.I ., Grohe, M., and Lukasiewicz, T

Reference 2

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This paper cites u ck, F., K \.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality u ck, F., K \

Reference 3

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This paper cites Subgraphormer: Unifying subgraph gnns and graph transformers via graph products.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Subgraphormer: Unifying subgraph gnns and graph transformers via graph products

Reference 4

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This paper cites A flexible, equivariant framework for subgraph gnns via graph products and graph coarsening.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality A flexible, equivariant framework for subgraph gnns via graph products and graph coarsening

Reference 5

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This paper cites and Klymko, C.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Klymko, C

Reference 6

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This paper cites On the limiting behavior of parameter-dependent network centrality measures.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality On the limiting behavior of parameter-dependent network centrality measures

Reference 7

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This paper cites M., and Maron, H.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality M., and Maron, H

Reference 8

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This paper cites Efficient subgraph gnns by learning effective selection policies.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Efficient subgraph gnns by learning effective selection policies

Reference 9

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Observation df8ebf65-7fb2-46bf-9126-7e5ebd9bbe2f · outbound

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

Reference 10

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Observation 0b776f98-4534-4813-a29d-6425f4edda7e · outbound

This paper cites F., and Bronstein, M.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality F., and Bronstein, M

Reference 11

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality F., Lio, P., and Bronstein, M

Reference 12

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This paper cites P., and Bronstein, M.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality P., and Bronstein, M

Reference 13

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This paper cites Residual Gated Graph ConvNets.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Residual Gated Graph ConvNets

Reference 14

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

Reference 15

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Observation 0ed110ad-0391-4459-b01e-6aa7a23adb52 · outbound

This paper cites Can graph neural networks count substructures? Advances in neural information processing systems, 33: 0 10383--10395, 2020.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Can graph neural networks count substructures? Advances in neural information processing systems, 33: 0 10383--10395, 2020

Reference 16

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This paper cites and Jegelka, S.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Jegelka, S

Reference 17

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This paper cites Reconstruction for powerful graph representations.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Reconstruction for powerful graph representations

Reference 18

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Approximation by superpositions of a sigmoidal function

Reference 19

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Graph Representation Learning with Individualization and Refinement

Reference 20

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

Reference 21

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality P., Luu, A

Reference 22

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality P., Ramp \'a s ek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A

Reference 23

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality P., Joshi, C

Reference 24

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This paper cites and Rodriguez-Velazquez, J.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Rodriguez-Velazquez, J

Reference 25

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Weber, M

Reference 26

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Lenssen, J

Reference 27

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Understanding and extending subgraph gnns by rethinking their symmetries

Reference 28

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

Reference 29

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Reutter, J

Reference 31

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality S., Riley, P

Reference 32

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality N., Duvenaud, D., Hern \'a ndez-Lobato, J

Reference 33

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality A new model for learning in graph domains

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality J., and Schult, D

Reference 35

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality A generalization of vit/mlp-mixer to graphs

Reference 36

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Approximation capabilities of multilayer feedforward networks

Reference 37

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Graph policy network for transferable active learning on graphs

Reference 38

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Open graph benchmark: Datasets for machine learning on graphs

Reference 39

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Strategies for pre-training graph neural networks

Reference 40

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

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Boosting the cycle counting power of graph neural networks with I ^2 -gnns

Reference 41

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Ribeiro, A

Reference 42

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality A new status index derived from sociometric analysis

Reference 43

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Mag-gnn: Reinforcement learning boosted graph neural network

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.462161Z

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Rethinking graph transformers with spectral attention

Reference 46

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Distance encoding: Design provably more powerful neural networks for graph representation learning

Reference 47

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality D., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S

Reference 48

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Expressive sign equivariant networks for spectral geometric learning

Reference 49

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Hutter, F

Reference 50

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Invariant and equivariant graph networks

Reference 51

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Neural network for graphs: A contextual constructive approach

Reference 52

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This paper cites L., Lenssen, J.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality L., Lenssen, J

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.321514Z

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Observation b5c4a337-592a-4204-83cb-6dafa09ca1c5 · outbound

This paper cites Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.301429Z

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Observation 45bf39b1-f4b8-473b-80c4-95172c179253 · outbound

This paper cites M., Grohe, M., Fey, M., and Borgwardt, K.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality M., Grohe, M., Fey, M., and Borgwardt, K

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.283658Z

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Observation 9a729a96-2696-40b1-b6cb-3153644515df · outbound

This paper cites an unresolved cited work.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:02:06.266913Z

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This paper cites A., Martinkus, K., Faber, L., and Wattenhofer, R.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality A., Martinkus, K., Faber, L., and Wattenhofer, R

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.248678Z

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Observation f6460321-a9e3-41b7-81ba-5d36a7230048 · outbound

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

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Pytorch: An imperative style, high-performance deep learning library

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Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.349348Z

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This paper cites Ordered subgraph aggregation networks.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Ordered subgraph aggregation networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.219961Z

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This paper cites P., Luu, A.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality P., Luu, A

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Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:02:06.184980Z

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Observation 1adafe14-d582-4f92-a7aa-ac7d8ac5ce5b · outbound

This paper cites Random features strengthen graph neural networks.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Random features strengthen graph neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.164499Z

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Observation 1ad7f734-bebf-44fb-8c61-2f80d50b34a5 · outbound

This paper cites C., Hagenbuchner, M., and Monfardini, G.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality C., Hagenbuchner, M., and Monfardini, G

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.378321Z

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Observation 9349b336-a152-4cb5-93d0-5172f663597b · outbound

This paper cites and Irwin, J.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Irwin, J

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.144602Z

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Observation 907326bc-c35d-4447-baf6-3b6bb5e3ecbd · outbound

This paper cites S., and He, L.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality S., and He, L

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.128122Z

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Observation 2c7ae1e5-c705-4eb7-a63b-792e8b8e1f2c · outbound

This paper cites Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.398595Z

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Observation 9470de83-2d7a-4926-85ef-d7c5df91215b · outbound

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

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Where did the gap go? reassessing the long-range graph benchmark

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.109456Z

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Observation c7faaf85-c54c-4caa-965f-81f8291dfe41 · outbound

This paper cites Building powerful and equivariant graph neural networks with structural message-passing.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Building powerful and equivariant graph neural networks with structural message-passing

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:06.088572Z

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Observation 49a7102f-1f73-406e-8a6d-3a0ab47bbae2 · outbound

This paper cites Equivariant and stable positional encoding for more powerful graph neural networks.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Equivariant and stable positional encoding for more powerful graph neural networks

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Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.417776Z

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Observation 23ba6c7f-4848-4326-9505-735d78867760 · outbound

This paper cites and Leman, A.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Leman, A

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.425499Z

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Observation 926c4e51-33dc-41e1-ad86-487e7a42eace · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations, 2018.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality How powerful are graph neural networks? In International Conference on Learning Representations, 2018

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Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.430947Z

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Observation 97b38f15-9e7f-41f0-ac69-f0a9360df9f2 · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34: 0 28877--28888, 2021.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34: 0 28877--28888, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.438126Z

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Observation 3f6e0e35-ea88-4585-af96-ffd3dfe01a70 · outbound

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

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality M., Ying, R., and Leskovec, J

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Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.444526Z

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Observation e1c712e4-3832-4580-85a6-2fe7b734892b · outbound

This paper cites Small relu networks are powerful memorizers: a tight analysis of memorization capacity.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Small relu networks are powerful memorizers: a tight analysis of memorization capacity

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:05.998528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a212fa4a-1ed2-400f-b112-c07e8353e9dd · outbound

This paper cites A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:05.978949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ef94a45c-7393-4c3f-97a4-80867b2c4609 · outbound

This paper cites Rethinking the expressive power of gnns via graph biconnectivity.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality Rethinking the expressive power of gnns via graph biconnectivity

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:05.959439Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 53521a86-0247-42ed-8d65-69269db2463a · outbound

This paper cites and Li, P.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality and Li, P

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T22:02:05.471815Z

Source-reported events for the cited work

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Observation f47cfec5-421c-4a6f-8f65-651391213b58 · outbound

This paper cites From stars to subgraphs: Uplifting any gnn with local structure awareness.

Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality From stars to subgraphs: Uplifting any gnn with local structure awareness

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:02:05.928634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 95031fd6-9f2f-4260-a266-d8994637dc6d · inbound

On the Expressive Power of Permutation-Equivariant Weight-Space Networks cites this paper.

On the Expressive Power of Permutation-Equivariant Weight-Space Networks Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T05:57:11.727855Z

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Observation 2c10eb82-1fa8-4003-ad95-24ccd5bf5e8a · inbound

On the Expressive Power of GNNs to Solve Linear SDPs cites this paper.

On the Expressive Power of GNNs to Solve Linear SDPs Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:30.281249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.