Heterogeneous Node Classification On Dblp 1

Metriken

Macro-F1 (20% training data)

Macro-F1 (60% training data)

Macro-F1 (80% training data)

Micro-F1 (20% training data)

Micro-F1 (80% training data)

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

						Paper Title
NLAH (2ndprox)	-	96.48%	-	-	-	Non-local Attention Learning on Large Heterogeneous Information Networks
NLAH (ppr)	-	95.95%	-	-	-	Non-local Attention Learning on Large Heterogeneous Information Networks
NLAH (ppmi)	-	95.91%	-	-	-	Non-local Attention Learning on Large Heterogeneous Information Networks
HAN	92.24%	93.70%	93.08%	93.11%	93.99%	Heterogeneous Graph Attention Network
HERec	91.68%	93.70%	92.34%	92.69%	93.27%	Heterogeneous Graph Attention Network
ESim	91.64%	93.39%	93.44%	92.73%	92.53%	Heterogeneous Graph Attention Network
HAN (sem)	92.03%	93.31%	92.53%	92.99%	93.29%	Heterogeneous Graph Attention Network
HAN (nd)	91.17%	92.69%	91.80%	92.05%	92.69%	Heterogeneous Graph Attention Network
GCN	90.79%	92.62%	92.38%	91.71%	93.09%	Heterogeneous Graph Attention Network
metapath2vec	90.16%	92.48%	91.89%	91.53%	92.80%	Heterogeneous Graph Attention Network
GAT	90.97%	91.84%	91.73%	91.96%	92.55%	Heterogeneous Graph Attention Network
DeepWalk	77.43%	85.27%	84.81%	79.37%	86.26%	Heterogeneous Graph Attention Network
HDGI-C	90.94%	-	91.53%	91.75%	92.26%	Heterogeneous Deep Graph Infomax
HDGI-A	89.88%	-	91.06%	90.62%	91.92%	Heterogeneous Deep Graph Infomax

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Heterogeneous Node Classification On Dblp 1 | SOTA | HyperAI