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

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Showing 801–825 of 1949 papers

TitleStatusHype
In-n-Out: Calibrating Graph Neural Networks for Link Prediction—0
DeepLink: A Novel Link Prediction Framework based on Deep Learning—0
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks—0
Input Snapshots Fusion for Scalable Discrete Dynamic Graph Nerual Networks—0
Halting in Random Walk Kernels—0
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques—0
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy—0
Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction—0
Harmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task—0
Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths—0
Degree-Based Random Walk Approach for Graph Embedding—0
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
Few-shot Link Prediction on N-ary Facts—0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning—0
Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction—0
Heterogeneous Graph Neural Network with Multi-view Representation Learning—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Few-shot graph link prediction with domain adaptation—0
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks—0
Conditional Network Embeddings—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
8ComplEx-N3 (reciprocal)Hits@100.96—Unverified
9MEI (small)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