SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1026–1050 of 1949 papers

TitleStatusHype
PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs—0
Poincaré Wasserstein Autoencoder—0
Polar Embedding—0
Applying Universal Schemas for Domain Specific Ontology Expansion—0
Power Efficient Discontinuous Reception in THz and mmWave Wireless Systems—0
PPKE: Knowledge Representation Learning by Path-based Pre-training—0
PPPNE: Personalized proximity preserved network embedding—0
Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method—0
Predict Anchor Links across Social Networks via an Embedding Approach—0
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning—0
Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion—0
Predicting Links on Wikipedia with Anchor Text Information—0
Diffusing Graph Attention—0
Predicting missing links via correlation between nodes—0
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding—0
A Preference Random Walk Algorithm for Link Prediction through Mutual Influence Nodes in Complex Networks—0
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks—0
Predicting the structure of dynamic graphs—0
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction—0
Premise Selection in Natural Language Mathematical Texts—0
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)—0
Pre-training Tasks for Embedding-based Large-scale Retrieval—0
Pretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings—0
Differentially Private Link Prediction With Protected Connections—0
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective—0
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All datasetsFB15k-237WN18RRWN18FB15kYAGO3-10ICEWS05-15ICEWS14CiteseerCoraPubmedWikidata5MGDELT

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoKGEHits@100.56—Unverified
2CP-N3-RPHits@100.55—Unverified
3DistMult (after variational EM)Hits@100.55—Unverified
4KG-R3Hits@100.54—Unverified
5LASSHits@100.53—Unverified
6MDE_advHits@100.53—Unverified
7GFA-NNHits@100.52—Unverified
8KGRefinerHits@100.49—Unverified
9ComplEx NSCachingHits@100.48—Unverified
10LogicENNHits@100.47—Unverified
#ModelMetricClaimedVerifiedStatus
1MoCoKGCHits@100.88—Unverified
2KERMITHits@100.83—Unverified
3MoCoSAHits@100.82—Unverified
4SimKGCIB(+PB+SN)Hits@100.82—Unverified
5C-LMKE(bert-base)Hits@100.79—Unverified
6LASSHits@100.79—Unverified
7LP-BERTHits@100.75—Unverified
8KGLMHits@100.74—Unverified
9StAR(Self-Adp)Hits@100.71—Unverified
10PALTHits@100.69—Unverified
#ModelMetricClaimedVerifiedStatus
1OpenKE (han2018openke)training time (s)11—Unverified
2LibKGE (ruffinelli2020you)training time (s)10—Unverified
3GraphVite (zhu2019graphvite)training time (s)6—Unverified
4Inverse ModelHits@100.96—Unverified
5QuatDEHits@100.96—Unverified
6LineaREHits@100.96—Unverified
7AutoKGEHits@100.96—Unverified
8MEI (small)Hits@100.96—Unverified
9ComplEx-N3 (reciprocal)Hits@100.96—Unverified
10RotatEHits@100.96—Unverified
#ModelMetricClaimedVerifiedStatus
1OPTransEHits@100.9—Unverified
2AutoKGEMRR0.86—Unverified
3ComplEx-N3 (reciprocal)MRR0.86—Unverified
4LineaREMRR0.84—Unverified
5DistMult (after variational EM)MRR0.84—Unverified
6QuatEMRR0.83—Unverified
7SEEKMRR0.83—Unverified
8MEI-BTDMRR0.81—Unverified
9MEI (small)MRR0.8—Unverified
10pRotatEMRR0.8—Unverified