Link Prediction

Link Prediction is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.

( Image credit: Inductive Representation Learning on Large Graphs )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1351–1360 of 1949 papers

Title	Date	Tasks	Status	Hype
Space-Time Correspondence as a Contrastive Random Walk	Jun 25, 2020	Dense Pixel Correspondence EstimationLink Prediction	CodeCode Available	1
A metric on directed graphs and Markov chains based on hitting probabilities	Jun 25, 2020	Dimensionality ReductionLink Prediction	CodeCode Available	0
Self-supervised edge features for improved Graph Neural Network training	Jun 23, 2020	General ClassificationGraph Attention	CodeCode Available	1
Non-Parametric Graph Learning for Bayesian Graph Neural Networks	Jun 23, 2020	Graph LearningLink Prediction	—Unverified	0
Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction	Jun 22, 2020	Link PredictionPrediction	—Unverified	0
A Self-Attention Network based Node Embedding Model	Jun 22, 2020	General ClassificationLink Prediction	CodeCode Available	1
Quantifying Challenges in the Application of Graph Representation Learning	Jun 18, 2020	Graph Representation LearningLink Prediction	—Unverified	0
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training	Jun 17, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1
Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks	Jun 12, 2020	ClusteringLink Prediction	CodeCode Available	0
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks	Jun 12, 2020	Feature EngineeringGraph Neural Network	—Unverified	0

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All datasets FB15k-237 WN18RR WN18 FB15k YAGO3-10 ICEWS05-15 ICEWS14 Citeseer Cora Pubmed Wikidata5M GDELT

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	AutoKGE	Hits@10	0.56	—	Unverified
2	CP-N3-RP	Hits@10	0.55	—	Unverified
3	DistMult (after variational EM)	Hits@10	0.55	—	Unverified
4	KG-R3	Hits@10	0.54	—	Unverified
5	LASS	Hits@10	0.53	—	Unverified
6	MDE_adv	Hits@10	0.53	—	Unverified
7	GFA-NN	Hits@10	0.52	—	Unverified
8	KGRefiner	Hits@10	0.49	—	Unverified
9	ComplEx NSCaching	Hits@10	0.48	—	Unverified
10	LogicENN	Hits@10	0.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MoCoKGC	Hits@10	0.88	—	Unverified
2	KERMIT	Hits@10	0.83	—	Unverified
3	MoCoSA	Hits@10	0.82	—	Unverified
4	SimKGCIB(+PB+SN)	Hits@10	0.82	—	Unverified
5	C-LMKE(bert-base)	Hits@10	0.79	—	Unverified
6	LASS	Hits@10	0.79	—	Unverified
7	LP-BERT	Hits@10	0.75	—	Unverified
8	KGLM	Hits@10	0.74	—	Unverified
9	StAR(Self-Adp)	Hits@10	0.71	—	Unverified
10	PALT	Hits@10	0.69	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OpenKE (han2018openke)	training time (s)	11	—	Unverified
2	LibKGE (ruffinelli2020you)	training time (s)	10	—	Unverified
3	GraphVite (zhu2019graphvite)	training time (s)	6	—	Unverified
4	Inverse Model	Hits@10	0.96	—	Unverified
5	QuatDE	Hits@10	0.96	—	Unverified
6	LineaRE	Hits@10	0.96	—	Unverified
7	AutoKGE	Hits@10	0.96	—	Unverified
8	MEI (small)	Hits@10	0.96	—	Unverified
9	ComplEx-N3 (reciprocal)	Hits@10	0.96	—	Unverified
10	RotatE	Hits@10	0.96	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OPTransE	Hits@10	0.9	—	Unverified
2	AutoKGE	MRR	0.86	—	Unverified
3	ComplEx-N3 (reciprocal)	MRR	0.86	—	Unverified
4	LineaRE	MRR	0.84	—	Unverified
5	DistMult (after variational EM)	MRR	0.84	—	Unverified
6	QuatE	MRR	0.83	—	Unverified
7	SEEK	MRR	0.83	—	Unverified
8	MEI-BTD	MRR	0.81	—	Unverified
9	MEI (small)	MRR	0.8	—	Unverified
10	pRotatE	MRR	0.8	—	Unverified