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 901–950 of 1949 papers

TitleStatusHype
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Community Detection Guarantees Using Embeddings Learned by Node2Vec—0
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding—0
Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach—0
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
Document Network Projection in Pretrained Word Embedding Space—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs—0
Large-Scale Network Embedding in Apache Spark—0
Latent Network Embedding via Adversarial Auto-encoders—0
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions—0
Incorporating GAN for Negative Sampling in Knowledge Representation Learning—0
DOLORES: Deep Contextualized Knowledge Graph Embeddings—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Incremental Learning on Growing Graphs—0
Community Aware Random Walk for Network Embedding—0
Knowlege Graph Embedding by Flexible Translation—0
Inductive Graph Embeddings through Locality Encodings—0
Domain Representation for Knowledge Graph Embedding—0
Pre-Trained Models for Heterogeneous Information Networks—0
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach—0
Kolmogorov-Arnold Graph Neural Networks—0
Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach—0
Exploring Graph Neural Networks for Indian Legal Judgment Prediction—0
Exploring Effects of Random Walk Based Minibatch Selection Policy on Knowledge Graph Completion—0
Combinatorial Scientific Discovery: Finding New Concept Combinations Beyond Link Prediction—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction—0
Inference over Unseen Entities, Relations and Literals on Knowledge Graphs—0
Knowledge Probing for Graph Representation Learning—0
Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction—0
Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs—0
Influence Maximization (IM) in Complex Networks with Limited Visibility Using Statistical Methods—0
Influence of Backdoor Paths on Causal Link Prediction—0
In-n-Out: Calibrating Graph Neural Networks for Link Prediction—0
Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction—0
InstantEmbedding: Efficient Local Node Representations—0
Instruction-based Hypergraph Pretraining—0
Integrating Heterogeneous Domain Information into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction—0
A Generative Model of Words and Relationships from Multiple Sources—0
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing—0
Combination of Unified Embedding Model and Observed Features for Knowledge Graph Completion—0
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering—0
GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models—0
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy—0
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs—0
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs—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