GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture | Paper Citation Record

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This paper cites Slic superpixels compared to state-of-the-art superpixel methods.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Slic superpixels compared to state-of-the-art superpixel methods

Reference 1

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This paper cites TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 2

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This paper cites Specformer: Spectral graph neural networks meet transformers.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Specformer: Spectral graph neural networks meet transformers

Reference 3

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This paper cites and Elisseeff, A.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture and Elisseeff, A

Reference 4

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This paper cites Residual Gated Graph ConvNets.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Residual Gated Graph ConvNets

Reference 5

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This paper cites I., Bronstein, M., Webb, S., and Rossi, E.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture I., Bronstein, M., Webb, S., and Rossi, E

Reference 6

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This paper cites Principal neighbourhood aggregation for graph nets.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Principal neighbourhood aggregation for graph nets

Reference 7

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This paper cites Recurrent distance filtering for graph representation learning.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Recurrent distance filtering for graph representation learning

Reference 8

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This paper cites S., Hou, K., Salakhutdinov, R.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture S., Hou, K., Salakhutdinov, R

Reference 9

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture P., Ramp \'a s ek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A

Reference 10

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This paper cites P., Joshi, C.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture P., Joshi, C

Reference 11

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This paper cites K., Winn, J., and Zisserman, A.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture K., Winn, J., and Zisserman, A

Reference 12

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This paper cites Were RNNs All We Needed?.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Were RNNs All We Needed?

Reference 13

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This paper cites and Lenssen, J.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture and Lenssen, J

Reference 14

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Understanding and extending subgraph gnns by rethinking their symmetries

Reference 15

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This paper cites A Large-Scale Database for Graph Representation Learning.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture A Large-Scale Database for Graph Representation Learning

Reference 16

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This paper cites and Leskovec, J.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture and Leskovec, J

Reference 17

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This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 18

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This paper cites Hippo: Recurrent memory with optimal polynomial projections.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Hippo: Recurrent memory with optimal polynomial projections

Reference 19

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This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 20

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This paper cites Inductive representation learning on large graphs.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Inductive representation learning on large graphs

Reference 21

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Open graph benchmark: Datasets for machine learning on graphs

Reference 22

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Boosting the cycle counting power of graph neural networks with i ^ 2 -gnns

Reference 23

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture On the Stability of Expressive Positional Encodings for Graphs

Reference 24

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This paper cites On the stability of expressive positional encodings for graph neural networks.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture On the stability of expressive positional encodings for graph neural networks

Reference 25

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This paper cites What Can We Learn from State Space Models for Machine Learning on Graphs?.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture What Can We Learn from State Space Models for Machine Learning on Graphs?

Reference 26

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Neural tangent kernel: Convergence and generalization in neural networks

Reference 27

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This paper cites R., Savarese, S., and Saxena, A.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture R., Savarese, S., and Saxena, A

Reference 28

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GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Unresolved cited work

Reference 29

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This paper cites Rethinking graph transformers with spectral attention.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Rethinking graph transformers with spectral attention

Reference 30

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Distance encoding: Design provably more powerful neural networks for graph representation learning

Reference 31

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This paper cites D., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture D., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S

Reference 32

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This paper cites K., Coates, M., Torr, P., and Lim, S.-N.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture K., Coates, M., Torr, P., and Lim, S.-N

Reference 33

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This paper cites L., Lenssen, J.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture L., Lenssen, J

Reference 34

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This paper cites Deepwalk: Online learning of social representations.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Deepwalk: Online learning of social representations

Reference 35

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This paper cites Graph Neural Ordinary Differential Equations.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Graph Neural Ordinary Differential Equations

Reference 36

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This paper cites P., Luu, A.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture P., Luu, A

Reference 37

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This paper cites Learnability, stability and uniform convergence.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Learnability, stability and uniform convergence

Reference 38

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This paper cites J., and Sinop, A.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture J., and Sinop, A

Reference 39

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This paper cites K., Bhalla, S., Usmani, S.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture K., Bhalla, S., Usmani, S

Reference 40

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GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Graph attention networks

Reference 41

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Equivariant and stable positional encoding for more powerful graph neural networks

Reference 42

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This paper cites State Space Model for New-Generation Network Alternative to Transformers: A Survey.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture State Space Model for New-Generation Network Alternative to Transformers: A Survey

Reference 43

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This paper cites and Cheng, Q.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture and Cheng, Q

Reference 44

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This paper cites N., Gomes, J., Geniesse, C., Pappu, A.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture N., Gomes, J., Geniesse, C., Pappu, A

Reference 45

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This paper cites Continuous graph neural networks.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Continuous graph neural networks

Reference 46

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture How powerful are graph neural networks? In International Conference on Learning Representations, 2018

Reference 47

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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.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34: 0 28877--28888, 2021

Reference 48

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GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Hierarchical graph representation learning with differentiable pooling

Reference 49

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This paper cites Position-aware graph neural networks.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Position-aware graph neural networks

Reference 50

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture M., Ying, R., and Leskovec, J

Reference 51

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This paper cites On the Expressive Power of Spectral Invariant Graph Neural Networks.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture On the Expressive Power of Spectral Invariant Graph Neural Networks

Reference 52

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This paper cites and Li, P.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture and Li, P

Reference 53

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

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture From stars to subgraphs: Uplifting any gnn with local structure awareness

Reference 54

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This paper cites C., and Dong, X.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture C., and Dong, X

Reference 55

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GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture write newline

Reference 56

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