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 1501–1550 of 1949 papers

TitleStatusHype
Probabilistic Knowledge Graph Embeddings—0
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs—0
Progresses and Challenges in Link Prediction—0
Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures—0
Projected Canonical Decomposition for Knowledge Base Completion—0
Promoting Fairness in Link Prediction with Graph Enhancement—0
Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness—0
Towards a graph-based foundation model for network traffic analysis—0
A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development—0
UWN: A Large Multilingual Lexical Knowledge Base—0
Towards Anticipation of Architectural Smells using Link Prediction Techniques—0
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations—0
PubGraph: A Large-Scale Scientific Knowledge Graph—0
PU Learning for Matrix Completion—0
Benchmarking Graph Neural Networks on Link Prediction—0
Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory—0
Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction—0
A Differential Geometric View and Explainability of GNN on Evolving Graphs—0
Quantifying Challenges in the Application of Graph Representation Learning—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction—0
AceKG: A Large-scale Knowledge Graph for Academic Data Mining—0
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
QUINT: Node embedding using network hashing—0
Bandit Sampling for Multiplex Networks—0
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction—0
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks—0
Balancing Augmentation with Edge-Utility Filter for Signed GNNs—0
Random Walks: A Review of Algorithms and Applications—0
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion—0
RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space—0
Towards Generating Explanations for ASP-Based Link Analysis using Declarative Program Transformations—0
Towards Learning Cross-Modal Perception-Trace Models—0
Reading The Web with Learned Syntactic-Semantic Inference Rules—0
Auxiliary learning induced graph convolutional networks—0
AutoWeird: Weird Translational Scoring Function Identified by Random Search—0
Automating psychological hypothesis generation with AI: when large language models meet causal graph—0
Automating Neural Architecture Design without Search—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Reconstructing commuters network using machine learning and urban indicators—0
RECS: Robust Graph Embedding Using Connection Subgraphs—0
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling—0
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs—0
Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning—0
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph—0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Redefining Developer Assistance: Through Large Language Models in Software Ecosystem—0
Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction—0
Refined Edge Usage of Graph Neural Networks for Edge Prediction—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