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算力平台
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SOTA
节点分类
Node Classification On Mutag
Node Classification On Mutag
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
Columns
模型名称
Accuracy
Paper Title
BoP
91.17
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
RR-GCN-PPV
79.41
R-GCN: The R Could Stand for Random
SCENE
75.44
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Path Tree
73.82
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph
R-GCN
73.23
Modeling Relational Data with Graph Convolutional Networks
RDF2Vec+SVM
67.20
RDF2Vec: RDF Graph Embeddings and Their Applications
0 of 6 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
节点分类
Node Classification On Mutag
Node Classification On Mutag
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
Columns
模型名称
Accuracy
Paper Title
BoP
91.17
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
RR-GCN-PPV
79.41
R-GCN: The R Could Stand for Random
SCENE
75.44
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Path Tree
73.82
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph
R-GCN
73.23
Modeling Relational Data with Graph Convolutional Networks
RDF2Vec+SVM
67.20
RDF2Vec: RDF Graph Embeddings and Their Applications
0 of 6 row(s) selected.
Previous
Next
Node Classification On Mutag | SOTA | HyperAI超神经