Research graph
References from SGFormer: Simplifying and Scaling Graph Transformers With Single-Layer Attention and Approximation-Free Linear Complexity. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1021/jm100574m
10.1021/jm100574m · External reference
10.1002/prot.22154
10.1002/prot.22154 · External reference
10.1145/1557019.1557108
10.1145/1557019.1557108 · External reference
Learning to simulate complex physics with graph networks
2020 · External reference
10.1109/cvpr46437.2021.00308
10.1109/cvpr46437.2021.00308 · External reference
A generalization of transformer networks to graphs
2020 · External reference
Graph-BERT: Only attention is needed for learning graph representations
2020 · External reference
Representing long-range context for graph neural networks with global attention
2021 · External reference
Do transformers really perform bad for graph representation?
2021 · External reference
10.52202/068431-1054
10.52202/068431-1054 · External reference
Gophormer: Ego-graph transformer for node classification
2021 · External reference
10.52202/068431-1986
10.52202/068431-1986 · External reference
NAGphormer: A tokenized graph transformer for node classification in large graphs
2023 · External reference
DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion
2023 · External reference
10.48550/arxiv.1706.03762
10.48550/arxiv.1706.03762 · External reference
10.1093/bib/bbab564
10.1093/bib/bbab564 · External reference
10.1021/acscatal.0c04525
10.1021/acscatal.0c04525 · External reference
10.1109/cvpr52688.2022.01071
10.1109/cvpr52688.2022.01071 · External reference
EquiFormer: Equivariant graph attention transformer for 3D atomistic graphs
2023 · External reference
10.1145/3477495.3532031
10.1145/3477495.3532031 · External reference
10.48550/arxiv.1810.04805
10.48550/arxiv.1810.04805 · External reference
Language models are few-shot learners
2020 · External reference
An image is worth 16 × 16 words: Transformers for image recognition at scale
2021 · External reference
Transformer for graphs: An overview from architecture perspective
2022 · External reference
OGB-LSC: A large-scale challenge for machine learning on graphs
2021 · External reference
Open graph benchmark: Datasets for machine learning on graphs
2020 · External reference
10.52202/075280-2826
10.52202/075280-2826 · External reference
10.52202/068431-1539
10.52202/068431-1539 · External reference
10.1109/tnn.2008.2005605
10.1109/tnn.2008.2005605 · External reference
Semi-supervised classification with graph convolutional networks
2017 · External reference
Graph attention networks
2018 · External reference
Representation learning on graphs with jumping knowledge networks
2018 · External reference
Simplifying graph convolutional networks
2019 · External reference
Beyond homophily in graph neural networks: Current limitations and effective designs
2020 · External reference
Finding global homophily in graph neural networks when meeting heterophily
2022 · External reference
10.1609/aaai.v35i12.17332
10.1609/aaai.v35i12.17332 · External reference
On the bottleneck of graph neural networks and its practical implications
2021 · External reference
Learning steady-states of iterative algorithms over graphs
2018 · External reference
Learning discrete structures for graph neural networks
2019 · External reference
Rethinking graph transformers with spectral attention
2021 · External reference
Structure-aware transformer for graph representation learning
2022 · External reference
10.1145/3534678.3539296
10.1145/3534678.3539296 · External reference
Inductive representation learning on large graphs
2017 · External reference
10.52202/068431-1060
10.52202/068431-1060 · External reference
10.1109/msp.2012.2235192
10.1109/msp.2012.2235192 · External reference
How to learn a graph from smooth signals
2016 · External reference
$p$p-Laplacian based graph neural networks
2022 · External reference
10.1007/11550518_45
10.1007/11550518_45 · External reference
Learning with local and global consistency
2004 · External reference
How expressive are transformers in spectral domain for graphs?
2022 · External reference
GraphSAINT: Graph sampling based inductive learning method
2020 · External reference
10.1145/3292500.3330925
10.1145/3292500.3330925 · External reference
GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings
2021 · External reference
10.1609/aimag.v29i3.2157
10.1609/aimag.v29i3.2157 · External reference
10.1145/1557019.1557108
10.1145/1557019.1557108 · External reference
10.1093/comnet/cnab014
10.1093/comnet/cnab014 · External reference
10.1145/3340531.3411866
10.1145/3340531.3411866 · External reference
10.1145/2783258.2783381
10.1145/2783258.2783381 · External reference
SNAP datasets: Stanford large network dataset collection
2014 · External reference
Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
2021 · External reference
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
2023 · External reference
Predict then propagate: Graph neural networks meet personalized PageRank
2019 · External reference
SIGN: Scalable inception graph neural networks
2020 · External reference
10.24963/ijcai.2020/694
10.24963/ijcai.2020/694 · External reference
10.1109/tpami.2020.3005590
10.1109/tpami.2020.3005590 · External reference
10.1109/tpami.2020.2989928
10.1109/tpami.2020.2989928 · External reference
