DSGCN-allfeat
|
Bridging the Gap Between Spectral and Spatial Dom…
|
78.39
|
2020-03-26
|
|
TFGW SP (L=2)
|
Template based Graph Neural Network with Optimal …
|
75.10
|
2022-05-31
|
|
Norm-GN
|
A New Perspective on the Effects of Spectrum in G…
|
73.33
|
2021-12-14
|
|
GDL-g (SP)
|
Online Graph Dictionary Learning
|
71.47
|
2021-02-12
|
|
HGP-SL
|
Hierarchical Graph Pooling with Structure Learning
|
68.79
|
2019-11-14
|
|
DSGCN-nodelabel
|
Bridging the Gap Between Spectral and Spatial Dom…
|
65.13
|
2020-03-26
|
|
WEGL
|
Wasserstein Embedding for Graph Learning
|
60.50
|
2020-06-16
|
|
TREE-G
|
TREE-G: Decision Trees Contesting Graph Neural Ne…
|
59.60
|
2022-07-06
|
|
GAT-GC (f-Scaled)
|
Improving Attention Mechanism in Graph Neural Net…
|
58.45
|
2019-07-04
|
|
R-GIN + PANDA
|
PANDA: Expanded Width-Aware Message Passing Beyon…
|
53.10
|
2024-06-06
|
|
Fea2Fea-s2
|
Fea2Fea: Exploring Structural Feature Correlation…
|
48.50
|
2021-06-24
|
|
GIN + PANDA
|
PANDA: Expanded Width-Aware Message Passing Beyon…
|
46.20
|
2024-06-06
|
|
R-GCN + PANDA
|
PANDA: Expanded Width-Aware Message Passing Beyon…
|
43.90
|
2024-06-06
|
|
GCN + PANDA
|
PANDA: Expanded Width-Aware Message Passing Beyon…
|
31.55
|
2024-06-06
|
|
DEMO-Net(weight)
|
DEMO-Net: Degree-specific Graph Neural Networks f…
|
27.20
|
2019-06-05
|
|
G-Tuning
|
Fine-tuning Graph Neural Networks by Preserving G…
|
26.70
|
2023-12-21
|
|
ESA (Edge set attention, no positional encodings)
|
An end-to-end attention-based approach for learni…
|
|
2024-02-16
|
|
GraphGPS
|
Recipe for a General, Powerful, Scalable Graph Tr…
|
|
2022-05-25
|
|
GAT
|
Graph Attention Networks
|
|
2017-10-30
|
|
GATv2
|
How Attentive are Graph Attention Networks?
|
|
2021-05-30
|
|
GCN
|
Semi-Supervised Classification with Graph Convolu…
|
|
2016-09-09
|
|
PNA
|
Principal Neighbourhood Aggregation for Graph Nets
|
|
2020-04-12
|
|
FGW sp
|
Optimal Transport for structured data with applic…
|
|
2018-05-23
|
|
GFN
|
Are Powerful Graph Neural Nets Necessary? A Disse…
|
|
2019-05-11
|
|
GFN-light
|
Are Powerful Graph Neural Nets Necessary? A Disse…
|
|
2019-05-11
|
|
GIN
|
How Powerful are Graph Neural Networks?
|
|
2018-10-01
|
|
G_Inception
|
When Work Matters: Transforming Classical Network…
|
|
2018-07-07
|
|
DUGNN
|
Learning Universal Graph Neural Network Embedding…
|
|
2019-09-22
|
|
UGT
|
Transitivity-Preserving Graph Representation Lear…
|
|
2023-08-18
|
|
GraphStar
|
Graph Star Net for Generalized Multi-Task Learning
|
|
2019-06-21
|
|
DropGIN
|
DropGNN: Random Dropouts Increase the Expressiven…
|
|
2021-11-11
|
|
EigenGCN-3
|
Graph Convolutional Networks with EigenPooling
|
|
2019-04-30
|
|
S2V (with 2 DiffPool)
|
Hierarchical Graph Representation Learning with D…
|
|
2018-06-22
|
|
GNN (DiffPool)
|
Hierarchical Graph Representation Learning with D…
|
|
2018-06-22
|
|
GIC
|
Gaussian-Induced Convolution for Graphs
|
|
2018-11-11
|
|
Multigraph ChebNet
|
Spectral Multigraph Networks for Discovering and …
|
|
2018-11-23
|
|
GIN
|
A Fair Comparison of Graph Neural Networks for Gr…
|
|
2019-12-20
|
|
WWL
|
Wasserstein Weisfeiler-Lehman Graph Kernels
|
|
2019-06-04
|
|
δ-2-LWL
|
Weisfeiler and Leman go sparse: Towards scalable …
|
|
2019-04-02
|
|
GraphSAGE
|
A Fair Comparison of Graph Neural Networks for Gr…
|
|
2019-12-20
|
|
DAGCN
|
DAGCN: Dual Attention Graph Convolutional Networks
|
|
2019-04-04
|
|
ECC (5 scores)
|
Dynamic Edge-Conditioned Filters in Convolutional…
|
|
2017-04-10
|
|
VRGC
|
Variational Recurrent Neural Networks for Graph C…
|
|
2019-02-07
|
|
NDP
|
Hierarchical Representation Learning in Graph Neu…
|
|
2019-10-24
|
|
SF + RFC
|
A Simple Baseline Algorithm for Graph Classificat…
|
|
2018-10-22
|
|
Local Topological Profile (LTP)
|
Strengthening structural baselines for graph clas…
|
|
2023-05-01
|
|
LDP
|
A simple yet effective baseline for non-attribute…
|
|
2018-11-08
|
|
BC + Capsules
|
Capsule Neural Networks for Graph Classification …
|
|
2019-02-22
|
|
1-NMFPool
|
A Non-Negative Factorization approach to node poo…
|
|
2019-09-07
|
|