Graph Classification
Graph classification is a task that categorizes graph-structured data into different classes or categories. The input is a graph, and the objective is to learn a classifier that can accurately predict the category to which the graph belongs. Graph classification has significant application value in areas such as social network analysis, bioinformatics, and recommendation systems.
sKNN-LDS
NDP
GTOT-Tuning
NDP
IsoNN
GDL-g (ADJ)
sKNN-LDS
CKGCN
NeuralWalker
sKNN-LDS
U2GNN (Unsupervised)
sKNN-LDS
GDL-g (ADJ)
CIN
sKNN-LDS
DSGCN-allfeat
GTOT-Tuning
CIN++
IsoNN
IsoNN
IsoNN
U2GNN (Unsupervised)
G-Tuning
GG-NN
GatedGCN+
NeuralWalker
G-Tuning
HGP-SL
EigenGCN-3
GAM
GAM
TFGW ADJ (L=2)
WKPI-kcenters
GAM
WKPI-kcenters
WKPI-kmeans
WKPI-kcenters
GraphMLPMixer
G-Tuning
U2GNN (Unsupervised)
Fea2Fea-s3
GFN-light
GIN-0
G-Tuning
CRaWl
R-GIN + PANDA
GNN (DiffPool)
GraphSAGE
Time-cohort Dynamic Features + Static Features
GMT
GMT
NeuroPath
UPFD-SAGE
HGFND
UGraphEmb-F
sKNN-LDS