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 651–700 of 1949 papers

TitleStatusHype
Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations—0
A Transfer Learning Framework for Multilayer Networks via Model Averaging—0
Higher-Order Relations Skew Link Prediction in Graphs—0
ALPINE: Active Link Prediction using Network Embedding—0
Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings—0
Higher-order Spectral Clustering for Heterogeneous Graphs—0
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation—0
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function—0
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs—0
Higher-order Structure Boosts Link Prediction on Temporal Graphs—0
How the use of feature selection methods influences the efficiency and accuracy of complex network simulations—0
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure—0
Hierarchical Hyperlink Prediction for the WWW—0
Continuous-time Graph Representation with Sequential Survival Process—0
Generating Human Understandable Explanations for Node Embeddings—0
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE—0
Aligning Knowledge and Text Embeddings by Entity Descriptions—0
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
A Hyperbolic-to-Hyperbolic Graph Convolutional Network—0
GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction—0
Gaussian Process Models for Link Analysis and Transfer Learning—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing—0
Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks—0
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
Context-Dependent Knowledge Graph Embedding—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models—0
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model—0
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding—0
GANE: A Generative Adversarial Network Embedding—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
Asymmetric Learning for Graph Neural Network based Link Prediction—0
Asymmetric Transitivity Preserving Graph Embedding—0
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction—0
From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Constructing Graph Node Embeddings via Discrimination of Similarity Distributions—0
Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production—0
From communities to interpretable network and word embedding: an unified approach—0
Generalized Reciprocal Perspective—0
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs—0
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Constraint-based Multi-hop Question Answering with Knowledge Graph—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—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