Graph Classification On Dd

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	Accuracy	Paper Title	Repository
1-NMFPool	76.0%	A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks	-
GraphStar	79.60%	Graph Star Net for Generalized Multi-Task Learning
DGCNN	79.37%	An End-to-End Deep Learning Architecture for Graph Classification
DGCNN	77.21%	DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model	-
GCN	78.151±3.465	Semi-Supervised Classification with Graph Convolutional Networks
EigenGCN-3	78.6%	Graph Convolutional Networks with EigenPooling
NDP	72%	Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
GATv2	75.966±2.191	How Attentive are Graph Attention Networks?
TokenGT	73.950±3.361	Pure Transformers are Powerful Graph Learners
GFN-light	78.62%	Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
DGK	73.50%	Deep Graph Kernels	-
PNA	78.992±4.407	Principal Neighbourhood Aggregation for Graph Nets
S2V (with 2 DiffPool)	82.07%	Hierarchical Graph Representation Learning with Differentiable Pooling
GAT	73.109±3.413	Graph Attention Networks
SEAL-SAGE	80.88%	Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
TREE-G	76.2%	TREE-G: Decision Trees Contesting Graph Neural Networks
GMT	78.72%	Accurate Learning of Graph Representations with Graph Multiset Pooling
U2GNN (Unsupervised)	95.67%	Universal Graph Transformer Self-Attention Networks
LDP + distance	77.5%	A simple yet effective baseline for non-attributed graph classification
Graph-JEPA	78.64%	Graph-level Representation Learning with Joint-Embedding Predictive Architectures

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