paper-with-me

Graph Classification 벤치마크

Graph Classification on PTC

74개 결과 · ⬇ CSV · JSON

Accuracy

56.41 65.26 74.11 82.96 91.81 2016-05 2026-11 PATCHY-SAN — 60.0 (2016-05-17) PATCHY-SAN — 60.0 (2016-05-17) graph2vec — 60.17 (2017-07-17) graph2vec — 60.17 (2017-07-17) DGCNN — 65.43 (2017-12-10) DGCNN — 65.43 (2017-12-10) G_DenseNet — 73.24 (2018-07-07) G_DenseNet — 73.24 (2018-07-07) GIN-0 — 64.4 (2018-10-01) GIN-0 — 64.4 (2018-10-01) SF + RFC — 62.8 (2018-10-22) SF + RFC — 62.8 (2018-10-22) LDP — 61.7 (2018-11-08) LDP — 61.7 (2018-11-08) GIC — 77.64 (2018-11-11) GIC — 77.64 (2018-11-11) DGA — 71.24 (2018-11-17) DGA — 71.24 (2018-11-17) BC + Capsules — 69.0 (2019-02-22) BC + Capsules — 69.0 (2019-02-22) Deep WL SGN(0,1,2) — 65.88 (2019-03-21) Deep WL SGN(0,1,2) — 65.88 (2019-03-21) UGraphEmb-F — 73.56 (2019-04-01) UGraphEmb — 72.54 (2019-04-01) UGraphEmb-F — 73.56 (2019-04-01) UGraphEmb — 72.54 (2019-04-01) δ-2-LWL — 62.7 (2019-04-02) δ-2-LWL — 62.7 (2019-04-02) DAGCN — 62.88 (2019-04-04) DAGCN — 62.88 (2019-04-04) SPI-GCN — 56.41 (2019-04-08) SPI-GCN — 56.41 (2019-04-08) DDGK — 63.14 (2019-04-21) DDGK — 63.14 (2019-04-21) sGIN — 73.56 (2019-05-21) sGIN — 73.56 (2019-05-21) PPGN — 66.17 (2019-05-27) PPGN — 66.17 (2019-05-27) WWL — 66.31 (2019-06-04) WWL — 66.31 (2019-06-04) hGANet — 65.02 (2019-07-05) cGANet — 63.53 (2019-07-05) GANet — 62.94 (2019-07-05) hGANet — 65.02 (2019-07-05) cGANet — 63.53 (2019-07-05) GANet — 62.94 (2019-07-05) IsoNN — 59.9 (2019-07-22) IsoNN — 59.9 (2019-07-22) InfoGraph — 61.65 (2019-07-31) InfoGraph — 61.65 (2019-07-31) DUGNN — 74.7 (2019-09-22) DUGNN — 74.7 (2019-09-22) U2GNN (Unsupervised) — 91.81 (2019-09-26) U2GNN — 69.63 (2019-09-26) U2GNN (Unsupervised) — 91.81 (2019-09-26) U2GNN — 69.63 (2019-09-26) SEG-BERT — 68.86 (2020-02-09) SEG-BERT — 68.86 (2020-02-09) WEGL — 67.5 (2020-06-16) WEGL — 67.5 (2020-06-16) DropGIN — 66.3 (2021-11-11) DropGIN — 66.3 (2021-11-11) Spec-GN — 68.05 (2021-12-14) Spec-GN — 68.05 (2021-12-14) TFGW ADJ (L=2) — 72.4 (2022-05-31) TFGW ADJ (L=2) — 72.4 (2022-05-31) TREE-G — 59.1 (2022-07-06) TREE-G — 59.1 (2022-07-06) CAN — 72.8 (2022-09-16) CAN — 72.8 (2022-09-16) CIN++ — 73.2 (2023-06-06) CIN++ — 73.2 (2023-06-06) GIUNet — 85.7 (2023-08-23) GIUNet — 85.7 (2023-08-23) PATCHY-SAN — 60.0 (2016-05-17) graph2vec — 60.17 (2017-07-17) DGCNN — 65.43 (2017-12-10) G_DenseNet — 73.24 (2018-07-07) GIC — 77.64 (2018-11-11) U2GNN (Unsupervised) — 91.81 (2019-09-26)
RankModel Accuracy Extra Training Data PaperCodeYear
1 U2GNN (Unsupervised) 91.81% Universal Graph Transformer Self-Attention Networks daiquocnguyen/Graph-Transformer 2019
2 GIUNet 85.7% Graph isomorphism UNet aramiracle/GIUNet 2023
3 GIC 77.64% Gaussian-Induced Convolution for Graphs 2018
4 DUGNN 74.7% ✓ Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning vermaMachineLearning/Universal-Graph-Embedding-Neural-Network 2019
5 UGraphEmb-F 73.56% Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity yunshengb/UGraphEmb 2019
5 sGIN 73.56% Mutual Information Maximization in Graph Neural Networks CODE-SUBMIT/Graph_Neighborhood_1 · CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network 2019
7 G_DenseNet 73.24% When Work Matters: Transforming Classical Network Structures to Graph CNN 2018
8 CIN++ 73.2% CIN++: Enhancing Topological Message Passing twitter-research/cwn 2023
9 CAN 72.8% Cell Attention Networks lrnzgiusti/can 2022
10 UGraphEmb 72.54% Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity yunshengb/UGraphEmb 2019
11 TFGW ADJ (L=2) 72.4% Template based Graph Neural Network with Optimal Transport Distances cedricvincentcuaz/TFGW 2022
12 DGA 71.24% Discriminative Graph Autoencoder 2018
13 U2GNN 69.63% Universal Graph Transformer Self-Attention Networks daiquocnguyen/Graph-Transformer 2019
14 BC + Capsules 69% Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations 2019
15 SEG-BERT 68.86% Segmented Graph-Bert for Graph Instance Modeling jwzhanggy/SEG-BERT 2020
16 Spec-GN 68.05% A New Perspective on the Effects of Spectrum in Graph Neural Networks qslim/gnn-spectrum 2021
17 WEGL 67.5% Wasserstein Embedding for Graph Learning navid-naderi/WEGL 2020
18 WWL 66.31% Wasserstein Weisfeiler-Lehman Graph Kernels BorgwardtLab/WWL · wenbintum/wwl-gpr 2019
19 DropGIN 66.3% DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks karolismart/dropgnn 2021
20 PPGN 66.17% Provably Powerful Graph Networks hadarser/ProvablyPowerfulGraphNetworks_torch · hadarser/ProvablyPowerfulGraphNetworks 2019
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