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 1526–1550 of 1949 papers

TitleStatusHype
RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data—0
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling—0
Network Embedding: An Overview—0
Effective Decoding in Graph Auto-Encoder using Triadic Closure—0
Time-aware Gradient Attack on Dynamic Network Link Prediction—0
Exponential Family Graph Embeddings—0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease AssociationsCode0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
Decompressing Knowledge Graph Representations for Link PredictionCode0
Equipping SBMs with RBMs: An Explainable Approach for Analysis of Networks with Covariates—0
A Re-evaluation of Knowledge Graph Completion MethodsCode0
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding—0
Neural Graph Embedding Methods for Natural Language ProcessingCode0
Relation Adversarial Network for Low Resource Knowledge Graph Completion—0
SENSE: Semantically Enhanced Node Sequence Embedding—0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
CoKE: Contextualized Knowledge Graph EmbeddingCode0
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
Dynamic Graph Embedding via LSTM History Tracking—0
Scalable Deep Generative Relational Models with High-Order Node Dependence—0
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature InteractionsCode0
CaRe: Open Knowledge Graph Embeddings—0
DRUM: End-To-End Differentiable Rule Mining On Knowledge GraphsCode0
A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?—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