Using Random Noise Equivariantly to Boost Graph Neural Networks Universally | Paper Citation Record

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally I., Grohe, M., and Lukasiewicz, T

Reference 1

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally \.I ., Grohe, M., and Lukasiewicz, T

Reference 2

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Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 3

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Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 4

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This paper cites and Yahav, E.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Yahav, E

Reference 5

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This paper cites G., Li, M.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally G., Li, M

Reference 6

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This paper cites Boosting Graph Neural Network Expressivity with Learnable Lanczos Constraints.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Boosting Graph Neural Network Expressivity with Learnable Lanczos Constraints

Reference 7

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This paper cites and Albert, R.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Albert, R

Reference 8

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This paper cites M., and Maron, H.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally M., and Maron, H

Reference 9

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This paper cites P., Shirobokov, S., Rossi, E., Frasca, F., Markovich, T., Hammerla, N., Bronstein, M.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally P., Shirobokov, S., Rossi, E., Frasca, F., Markovich, T., Hammerla, N., Bronstein, M

Reference 10

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This paper cites P., Shirobokov, S., Rossi, E., Frasca, F., Markovich, T., Hammerla, N., Bronstein, M.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally P., Shirobokov, S., Rossi, E., Frasca, F., Markovich, T., Hammerla, N., Bronstein, M

Reference 11

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This paper cites Fastgcn: Fast learning with graph convolutional networks via importance sampling.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Fastgcn: Fast learning with graph convolutional networks via importance sampling

Reference 12

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This paper cites Adaptive universal generalized pagerank graph neural network.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Adaptive universal generalized pagerank graph neural network

Reference 13

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Observation e5386c8f-348b-48cc-a6f5-63b28ee4c7c2 · outbound

This paper cites Principal neighbourhood aggregation for graph nets.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Principal neighbourhood aggregation for graph nets

Reference 14

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This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Convolutional neural networks on graphs with fast localized spectral filtering

Reference 15

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This paper cites Pure Message Passing Can Estimate Common Neighbor for Link Prediction.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Pure Message Passing Can Estimate Common Neighbor for Link Prediction

Reference 16

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Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 17

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This paper cites P., Luu, A.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally P., Luu, A

Reference 18

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This paper cites P., Ramp \' a sek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally P., Ramp \' a sek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A

Reference 19

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This paper cites and Maron, H.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Maron, H

Reference 20

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Observation d68b1570-6abe-406a-89f3-b4d61d145aa7 · outbound

This paper cites Protein interface prediction using graph convolutional networks.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Protein interface prediction using graph convolutional networks

Reference 21

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This paper cites M., and Maron, H.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally M., and Maron, H

Reference 22

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This paper cites S., Riley, P.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally S., Riley, P

Reference 23

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This paper cites M., Aguilera - Iparraguirre, J., Hirzel, T.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally M., Aguilera - Iparraguirre, J., Hirzel, T

Reference 24

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally L., Ying, R., and Leskovec, J

Reference 25

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This paper cites Bernnet: Learning arbitrary graph spectral filters via bernstein approximation.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Bernnet: Learning arbitrary graph spectral filters via bernstein approximation

Reference 26

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This paper cites Open graph benchmark: Datasets for machine learning on graphs.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Open graph benchmark: Datasets for machine learning on graphs

Reference 27

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This paper cites On the Stability of Expressive Positional Encodings for Graphs.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally On the Stability of Expressive Positional Encodings for Graphs

Reference 28

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Boosting the cycle counting power of graph neural networks with i \^ 2 -gnns

Reference 29

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This paper cites Transformers generalize deepsets and can be extended to graphs & hypergraphs.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Transformers generalize deepsets and can be extended to graphs & hypergraphs

Reference 30

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Semi-Supervised Classification with Graph Convolutional Networks

Reference 31

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This paper cites Predict then propagate: Graph neural networks meet personalized pagerank.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Predict then propagate: Graph neural networks meet personalized pagerank

Reference 32

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This paper cites L., L \' e tourneau, V., and Tossou, P.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally L., L \' e tourneau, V., and Tossou, P

Reference 33

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This paper cites Distance encoding: Design provably more powerful neural networks for graph representation learning.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Distance encoding: Design provably more powerful neural networks for graph representation learning

Reference 34

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Observation a5ffb695-4577-4c47-8da8-62f5ecd20299 · outbound

This paper cites Deeper insights into graph convolutional networks for semi-supervised learning.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Deeper insights into graph convolutional networks for semi-supervised learning

Reference 35

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This paper cites D., Zhao, L., Smidt, T.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally D., Zhao, L., Smidt, T

Reference 36

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This paper cites Laplacian canonization: A minimalist approach to sign and basis invariant spectral embedding.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Laplacian canonization: A minimalist approach to sign and basis invariant spectral embedding

Reference 37

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This paper cites Provably powerful graph networks.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Provably powerful graph networks

Reference 38

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Observation 50bf2af1-4d19-420f-95d2-c269a3f58152 · outbound

This paper cites Invariant and equivariant graph networks.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Invariant and equivariant graph networks

Reference 39

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This paper cites On learning sets of symmetric elements.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally On learning sets of symmetric elements

Reference 40

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Observation c7aa7cc1-1cc9-4ce3-b973-2be8582b310e · outbound

This paper cites Graphit: Encoding graph structure in transformers, 2021.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Graphit: Encoding graph structure in transformers, 2021

Reference 41

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally A., Martinkus, K., Faber, L., and Wattenhofer, R

Reference 42

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This paper cites C., Lei, Y., and Yang, B.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally C., Lei, Y., and Yang, B

Reference 43

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Observation 29ea73af-1dcb-4591-b04e-1624dd24a13c · outbound

This paper cites Ordered subgraph aggregation networks.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Ordered subgraph aggregation networks

Reference 44

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Observation c08320ac-3ebf-4b77-bd17-9c34b13b9e81 · outbound

This paper cites P., Luu, A.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally P., Luu, A

Reference 45

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Observation dabf1181-ace5-4cef-9e36-88b2b8e4f595 · outbound

This paper cites Multi-scale attributed node embedding.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Multi-scale attributed node embedding

Reference 46

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This paper cites Random features strengthen graph neural networks.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Random features strengthen graph neural networks

Reference 47

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Observation c30cb4d1-5225-4892-b458-8192f1ac4180 · outbound

This paper cites and Lipman, Y.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Lipman, Y

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.726337Z

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Observation 8dd41f05-c1cd-450e-be3d-c453d7709938 · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Pitfalls of Graph Neural Network Evaluation

Reference 49

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Observation 9311c9b9-dbfb-4046-8c80-7d0a8f0ba9a7 · outbound

This paper cites J., and Sinop, A.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally J., and Sinop, A

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.708782Z

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Observation 80eb31f1-0c75-4c9a-b302-785a98d41a59 · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Equivariant and stable positional encoding for more powerful graph neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.687501Z

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Observation 55bcf427-5ea9-4299-b343-1f23778b6cc0 · outbound

This paper cites and Zhang, M.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Zhang, M

Reference 52

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Observation 2faa3ec8-ae8b-43b2-980e-bc690cf2c765 · outbound

This paper cites and Zhang, M.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Zhang, M

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.645149Z

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Observation 137bfce6-11ad-4c9a-a6cf-89845fe1b2ae · outbound

This paper cites PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:05:56.078413Z

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Observation dfd2ed42-d602-45ec-a16a-9319b4b57eb8 · outbound

This paper cites Graph as point set.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Graph as point set

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.628064Z

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 20b4cdcc-8b2d-46b7-91e3-b291ce85318f · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Neural common neighbor with completion for link prediction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.611592Z

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 b58f09e7-3fd0-4f2c-aac4-d7372b261809 · outbound

This paper cites A., Mirhoseini, A., Gonzalez, J.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally A., Mirhoseini, A., Gonzalez, J

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.589383Z

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 eaf57bf2-d627-450c-bb24-2fda0fa5aa7f · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally How powerful are graph neural networks? In ICLR, 2019

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T12:05:55.923412Z

Source-reported events for the cited work

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Observation eb093a2e-8517-490e-8e2b-753dfdf11a88 · outbound

This paper cites W., and Salakhutdinov, R.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally W., and Salakhutdinov, R

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.555164Z

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 8760fddb-87f5-4e32-99ea-f224ed31b40e · outbound

This paper cites Graph convolutional networks for text classification.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Graph convolutional networks for text classification

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.536477Z

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 a864d37c-69e5-4d2a-bed0-f0261b7f5c3e · outbound

This paper cites Do transformers really perform badly for graph representation? In NeurIPS, 2021.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Do transformers really perform badly for graph representation? In NeurIPS, 2021

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.519147Z

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 9245f21b-c2ad-4aa3-99d9-4910ab16065e · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally M., Ying, R., and Leskovec, J

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.499616Z

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 da474363-96f5-4d3a-ba69-12df18ca741f · outbound

This paper cites an unresolved cited work.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:05:56.472967Z

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 b5dec0e6-4760-4b7d-9cf0-0a3fba1eca6c · outbound

This paper cites an unresolved cited work.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:05:56.449976Z

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 1d85cb84-3233-462c-b7ea-3368d58abd68 · outbound

This paper cites an unresolved cited work.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:05:56.433596Z

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 8dfc68ec-98e6-4a64-9677-501fd574d6f6 · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.415975Z

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 88f907a4-09e7-4799-92ca-a821df2b249e · outbound

This paper cites and Chen, Y.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Chen, Y

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.394973Z

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 bf49b103-99c9-4a29-872c-30507d5f42fc · outbound

This paper cites and Li, P.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally and Li, P

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.360833Z

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 750ed9ec-8d62-4426-8425-219b20db3dd2 · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Labeling trick: A theory of using graph neural networks for multi-node representation learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.340548Z

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 a78bd93c-3cbe-4229-a1db-8796d64509a9 · outbound

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

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally From stars to subgraphs: Uplifting any GNN with local structure awareness

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.321440Z

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 05051c4d-e43e-4851-8822-4ad2978ea9ab · outbound

This paper cites Distance-restricted folklore weisfeiler-leman gnns with provable cycle counting power, 2023.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Distance-restricted folklore weisfeiler-leman gnns with provable cycle counting power, 2023

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.299748Z

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 86fa72fc-68f2-48d9-9cb0-a973c9b8c566 · outbound

This paper cites Predicting missing links via local information.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Predicting missing links via local information

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:05:56.277360Z

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 f3d6eb0b-f5e0-4598-b68d-6b95f912db54 · outbound

This paper cites an unresolved cited work.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-09T12:05:56.250466Z

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 0986fe36-8b5d-4b42-98dd-cec80e187a76 · outbound

This paper cites write newline.

Using Random Noise Equivariantly to Boost Graph Neural Networks Universally write newline

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-09T12:05:56.019965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

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