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 551–575 of 1949 papers

TitleStatusHype
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Bandit Sampling for Multiplex Networks—0
Diffusing Graph Attention—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Graph Attention Inference of Network Topology in Multi-Agent Systems—0
Anchor Prediction: A Topic Modeling Approach—0
A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature—0
Balancing Augmentation with Edge-Utility Filter for Signed GNNs—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Clustering with Graph Neural Networks—0
Neural graph embeddings as explicit low-rank matrix factorization for link prediction—0
GNN Applied to Ego-nets for Friend Suggestions—0
Auxiliary learning induced graph convolutional networks—0
Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs—0
AutoWeird: Weird Translational Scoring Function Identified by Random Search—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
GRADE: Graph Dynamic Embedding—0
An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity—0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
Automating psychological hypothesis generation with AI: when large language models meet causal graph—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
Degree-Based Random Walk Approach for Graph Embedding—0
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning—0
Automating Neural Architecture Design without Search—0
DeepTrax: Embedding Graphs of Financial Transactions—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
8ComplEx-N3 (reciprocal)Hits@100.96—Unverified
9MEI (small)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