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 1651–1700 of 1949 papers

TitleStatusHype
Link Prediction in Hypergraphs using Graph Convolutional Networks—0
DyRep: Learning Representations over Dynamic GraphsCode0
Scalable Neural Theorem Proving on Knowledge Bases and Natural Language—0
Graph Convolutional Networks with EigenPoolingCode0
On the Use of ArXiv as a DatasetCode0
Soft Marginal TransE for Scholarly Knowledge Graph Completion—0
Quaternion Knowledge Graph EmbeddingsCode0
Link Prediction in Multiplex Networks based on Interlayer Similarity—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Deep Representation Learning for Social Network Analysis—0
Compositional Network Embedding—0
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge GraphsCode0
FILDNE: A Framework for Incremental Learning of Dynamic Networks EmbeddingsCode0
Graph Pattern Entity Ranking Model for Knowledge Graph Completion—0
GNE: a deep learning framework for gene network inference by aggregating biological informationCode0
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning—0
PyTorch-BigGraph: A Large-scale Graph Embedding SystemCode0
Domain Representation for Knowledge Graph Embedding—0
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation ExtractionCode0
Node Embedding over Temporal GraphsCode0
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning—0
A Comparative Study for Unsupervised Network Representation Learning—0
Splitter: Learning Node Representations that Capture Multiple Social ContextsCode0
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs—0
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measuresCode0
Learning Edge Properties in Graphs from Path AggregationsCode0
Logic Rules Powered Knowledge Graph Embedding—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
Signed Link Prediction with Sparse Data: The Role of Personality Information—0
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
Link Prediction with Mutual Attention for Text-Attributed Networks—0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Web Links Prediction And Category-Wise Recommendation Based On Browser HistoryCode0
Learning Topological Representation for Networks via Hierarchical SamplingCode0
Adaptive Sequence SubmodularityCode0
Link Prediction via Higher-Order Motif FeaturesCode0
Heterogeneous Edge Embeddings for Friend Recommendation—0
A Relational Tucker Decomposition for Multi-Relational Link Prediction—0
Representation Learning for Heterogeneous Information Networks via Embedding EventsCode0
GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic NetworksCode0
Topological and Semantic Graph-based Author Disambiguation on DBLP Data in Neo4j—0
Attributed Network Embedding via Subspace DiscoveryCode0
Poincaré Wasserstein Autoencoder—0
Learning Graph Embedding with Adversarial Training Methods—0
Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality—0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Embedding Cardinality Constraints in Neural Link Predictors—0
Adversarial Autoencoders with Constant-Curvature Latent ManifoldsCode0
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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