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 451–475 of 1949 papers

TitleStatusHype
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search—0
Few-shot Link Prediction on N-ary Facts—0
Finding structural hole spanners based on community forest model and diminishing marginal utility in large scale social networks—0
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction—0
Big Networks: A Survey—0
Few-shot graph link prediction with domain adaptation—0
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction—0
BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder—0
Feature Learning for Meta-Paths in Knowledge Graphs—0
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information—0
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach—0
Domain Representation for Knowledge Graph Embedding—0
Domain Generalization Deep Graph Transformation—0
Fast Computation of Katz Index for Efficient Processing of Link Prediction Queries—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network—0
DOLORES: Deep Contextualized Knowledge Graph Embeddings—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs—0
Drug Re-positioning via Text Augmented Knowledge Graph Embeddings—0
Does Link Prediction Help Detect Feature Interactions in Software Product Lines (SPLs)?—0
Bi-Link: Bridging Inductive Link Predictions from Text via Contrastive Learning of Transformers and Prompts—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Show:102550
← PrevPage 19 of 78Next →
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