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
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Trans4E: Link Prediction on Scholarly Knowledge Graphs—0
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective—0
Edge Proposal Sets for Link PredictionCode1
Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition—0
Leveraging Static Models for Link Prediction in Temporal Knowledge Graphs—0
Convolutional Hypercomplex Embeddings for Link PredictionCode0
KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods—0
OKGIT: Open Knowledge Graph Link Prediction with Implicit TypesCode0
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge GraphsCode1
Neural Concept Formation in Knowledge GraphsCode0
Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction—0
A Deep Latent Space Model for Graph Representation LearningCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Large-Scale Network Embedding in Apache Spark—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs—0
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical StudyCode1
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
Query Embedding on Hyper-relational Knowledge GraphsCode1
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph EmbeddingCode0
Node Classification Meets Link Prediction on Knowledge Graphs—0
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link PredictionCode1
Correcting Exposure Bias for Link RecommendationCode0
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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