← Heterogeneous Node Classification

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DBLP (PACT) 14k

아카이브 평가 14건 · 마지막 적재/변경 2026-10-30

원본 데이터셋 링크 1

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
HAN	Micro-F1 (20% training data) 93.11% Macro-F1 (20% training data) 92.24% Macro-F1 (60% training data) 93.70% Micro-F1 (80% training data) 93.99% Macro-F1 (80% training data) 93.08%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
HAN (sem)	Micro-F1 (20% training data) 92.99% Macro-F1 (20% training data) 92.03% Macro-F1 (60% training data) 93.31% Micro-F1 (80% training data) 93.29% Macro-F1 (80% training data) 92.53%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
ESim	Micro-F1 (20% training data) 92.73% Macro-F1 (20% training data) 91.64% Macro-F1 (60% training data) 93.39% Micro-F1 (80% training data) 92.53% Macro-F1 (80% training data) 93.44%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
HERec	Micro-F1 (20% training data) 92.69% Macro-F1 (20% training data) 91.68% Macro-F1 (60% training data) 93.70% Micro-F1 (80% training data) 93.27% Macro-F1 (80% training data) 92.34%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
HAN (nd)	Micro-F1 (20% training data) 92.05% Macro-F1 (20% training data) 91.17% Macro-F1 (60% training data) 92.69% Micro-F1 (80% training data) 92.69% Macro-F1 (80% training data) 91.80%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
GAT	Micro-F1 (20% training data) 91.96% Macro-F1 (20% training data) 90.97% Macro-F1 (60% training data) 91.84% Micro-F1 (80% training data) 92.55% Macro-F1 (80% training data) 91.73%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
HDGI-C	Micro-F1 (20% training data) 91.75% Macro-F1 (20% training data) 90.94% Micro-F1 (80% training data) 92.26% Macro-F1 (80% training data) 91.53%	Heterogeneous Deep Graph Infomax 2019-11-19	코드 1
GCN	Micro-F1 (20% training data) 91.71% Macro-F1 (20% training data) 90.79% Macro-F1 (60% training data) 92.62% Micro-F1 (80% training data) 93.09% Macro-F1 (80% training data) 92.38%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
metapath2vec	Micro-F1 (20% training data) 91.53% Macro-F1 (20% training data) 90.16% Macro-F1 (60% training data) 92.48% Micro-F1 (80% training data) 92.80% Macro-F1 (80% training data) 91.89%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
HDGI-A	Micro-F1 (20% training data) 90.62% Macro-F1 (20% training data) 89.88% Micro-F1 (80% training data) 91.92% Macro-F1 (80% training data) 91.06%	Heterogeneous Deep Graph Infomax 2019-11-19	코드 1
DeepWalk	Micro-F1 (20% training data) 79.37% Macro-F1 (20% training data) 77.43% Macro-F1 (60% training data) 85.27% Micro-F1 (80% training data) 86.26% Macro-F1 (80% training data) 84.81%	Heterogeneous Graph Attention Network 2019-03-18	코드 1 · 코드 2 · 코드 3 · 코드 4
NLAH (2ndprox)	Macro-F1 (60% training data) 96.48%	Non-local Attention Learning on Large Heterogeneous Information Networks 2019-12-12	코드 1
NLAH (ppr)	Macro-F1 (60% training data) 95.95%	Non-local Attention Learning on Large Heterogeneous Information Networks 2019-12-12	코드 1
NLAH (ppmi)	Macro-F1 (60% training data) 95.91%	Non-local Attention Learning on Large Heterogeneous Information Networks 2019-12-12	코드 1