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 1151–1200 of 1949 papers

TitleStatusHype
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation—0
Decoupling feature propagation from the design of graph auto-encoders—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation—0
Link Prediction with Physics-Inspired Graph Neural Networks—0
Link Prediction using Embedded Knowledge Graphs—0
Link Prediction using Graph Neural Networks for Master Data Management—0
Link Prediction via Graph Attention Network—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Link Prediction via Matrix Completion—0
Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models—0
Link Prediction with Contextualized Self-Supervision—0
Link prediction with continuous-time classical and quantum walks—0
Link Prediction with Mutual Attention for Text-Attributed Networks—0
Analysis of Drug repurposing Knowledge graphs for Covid-19—0
Data Poisoning Attack against Unsupervised Node Embedding Methods—0
Link Prediction with Relational Hypergraphs—0
Link Prediction with Social Vector Clocks—0
Link Prediction with Untrained Message Passing Layers—0
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case—0
LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs—0
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
Data Augmentation for Personal Knowledge Base Population—0
Statistical Guarantees for Link Prediction using Graph Neural Networks—0
Stochastic Blockmodels meet Graph Neural Networks—0
Stochastic Block Models with Multiple Continuous Attributes—0
Local Structure-aware Graph Contrastive Representation Learning—0
Locate Who You Are: Matching Geo-location to Text for User Identity Linkage—0
Location Sensitive Embedding for Knowledge Graph Reasoning—0
Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation—0
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach—0
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules—0
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules—0
Logic Rules Powered Knowledge Graph Embedding—0
Log-Normal Matrix Completion for Large Scale Link Prediction—0
CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Network—0
Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality—0
Love tHy Neighbour: Remeasuring Local Structural Node Similarity in Hypergraph-Derived Networks—0
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
Low Resource Quadratic Forms for Knowledge Graph Embeddings—0
Understanding Generalization in Node and Link Prediction—0
Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space—0
Cross view link prediction by learning noise-resilient representation consensus—0
Cross-Network Learning with Partially Aligned Graph Convolutional Networks—0
Cross-Domain Recommendation via Preference Propagation GraphNet—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Understanding graph embedding methods and their applications—0
Manifold Rewiring for Unlabeled Imaging—0
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach—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