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 801–850 of 1949 papers

TitleStatusHype
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks—0
Deep Insights into Noisy Pseudo Labeling on Graph DataCode0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—0
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link PredictionCode0
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge GraphsCode0
SANGEA: Scalable and Attributed Network Generation—0
TouchUp-G: Improving Feature Representation through Graph-Centric FinetuningCode0
Knowledge Graph Embedding: An Overview—0
Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction—0
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
Temporal Smoothness Regularisers for Neural Link Predictors—0
Human Action Co-occurrence in Lifestyle Vlogs using Graph Link PredictionCode0
A parameterised model for link prediction using node centrality and similarity measure based on graph embedding—0
Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?Code0
Force-directed graph embedding with hops distanceCode0
End-to-End Learning on Multimodal Knowledge GraphsCode0
A Text-based Approach For Link Prediction on Wikipedia ArticlesCode0
Link Prediction for Wikipedia Articles as a Natural Language Inference TaskCode0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Over-Squashing in Graph Neural Networks: A Comprehensive survey—0
Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More—0
Relational Concept Bottleneck ModelsCode0
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic GraphsCode0
Geometric instability of graph neural networks on large graphsCode0
Development of a Knowledge Graph Embeddings Model for PainCode0
Modeling Edge Features with Deep Bayesian Graph NetworksCode0
Independent Distribution Regularization for Private Graph EmbeddingCode0
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models—0
Inductive Knowledge Graph Completion with GNNs and Rules: An AnalysisCode0
Context-aware Event Forecasting via Graph DisentanglementCode0
Local Structure-aware Graph Contrastive Representation Learning—0
Evaluating Link Prediction Explanations for Graph Neural NetworksCode0
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case—0
Towards Semantically Enriched Embeddings for Knowledge Graph Completion—0
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation—0
HUGE: Huge Unsupervised Graph Embeddings with TPUs—0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph TransformersCode0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach—0
Temporal Fact Reasoning over Hyper-Relational Knowledge GraphsCode0
A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes—0
Learning from Heterogeneity: A Dynamic Learning Framework for HypergraphsCode0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks—0
A Survey on Graph Classification and Link Prediction based on GNN—0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edgeCode0
Conformal link prediction for false discovery rate controlCode0
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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