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 751–775 of 1949 papers

TitleStatusHype
Hyperlink Regression via Bregman Divergence—0
Neural graph embeddings as explicit low-rank matrix factorization for link prediction—0
Graph Embedding with Hierarchical Attentive Membership—0
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
GraphGAN: Generating Graphs via Random Walks—0
Data Augmentation for Personal Knowledge Base Population—0
Graph Generation via Scattering—0
Forward Backward Similarity Search in Knowledge Networks—0
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation—0
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding—0
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings—0
FOBE and HOBE: First- and High-Order Bipartite Embeddings—0
Graph Learning: A Survey—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction—0
Adaptive Convolution for Multi-Relational Learning—0
HyperQuery: Beyond Binary Link Prediction—0
Decoupling feature propagation from the design of graph auto-encoders—0
Graph Neural Networks at a Fraction—0
FMGNN: Fused Manifold Graph Neural Network—0
Flashlight: Scalable Link Prediction with Effective Decoders—0
Conformal retrofitting via Riemannian manifolds: distilling task-specific graphs into pretrained embeddings—0
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks—0
A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?—0
Show:102550
← PrevPage 31 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