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Link Property Prediction On Ogbl Biokg

Métriques

Number of params

Test MRR

Validation MRR

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Number of params	Test MRR	Validation MRR	Paper Title	Repository
ComplEx-N3-RP	187750000	0.8494	0.8497	Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations	-
TransE	187648000	0.7452 ± 0.0004	0.7456 ± 0.0003	-	-
ComplEx	187648000	0.8095 ± 0.0007	0.8105 ± 0.0001	Complex Embeddings for Simple Link Prediction	-
GFA-NN	-	0.9011	0.9011	Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities	-
DistMult	187648000	0.8043 ± 0.0003	0.8055 ± 0.0003	Embedding Entities and Relations for Learning and Inference in Knowledge Bases	-
ComplEx^2	187648000	0.8583 ± 0.0005	0.8592 ± 0.0004	How to Turn Your Knowledge Graph Embeddings into Generative Models	-
ComplEx-RP (1000dim)	187750000	0.8492 ± 0.0002	0.8497 ± 0.0002	-	-
ComplEx^2	187648000	0.8583 ± 0.0005	0.8592 ± 0.0004	-	-
UniBi	181654170	0.8550 ± 0.0003	0.8553 ± 0.0001	Prior Bilinear Based Models for Knowledge Graph Completion	-
RelEns	849427106	0.9618 ± 0.0002	0.9627 ± 0.0004	Relation-aware Ensemble Learning for Knowledge Graph Embedding	-
AutoBLM-KGBench	192047104	0.8536 ± 0.0003	0.8548 ± 0.0002	Bilinear Scoring Function Search for Knowledge Graph Learning	-
AutoSF	93824000	0.8309 ± 0.0008	0.8317 ± 0.0007	AutoSF: Searching Scoring Functions for Knowledge Graph Embedding	-
PairRE	187750000	0.8164 ± 0.0005	0.8172 ± 0.0005	PairRE: Knowledge Graph Embeddings via Paired Relation Vectors	-
NBFNet	734,209	0.8317	0.8318	Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction	-
TripleRE	469630002	0.8348 ± 0.0007	0.8360 ± 0.0006	-	-
RotatE	187597000	0.7989 ± 0.0004	0.7997 ± 0.0002	RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space	-

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Link Property Prediction On Ogbl Biokg | SOTA | HyperAI