SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 876–900 of 982 papers

TitleStatusHype
Quantifying Challenges in the Application of Graph Representation Learning—0
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
Graph Representation Learning Network via Adaptive SamplingCode0
Deep Graph Contrastive Representation LearningCode1
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
Understanding Negative Sampling in Graph Representation LearningCode1
A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks—0
Machine Learning on Graphs: A Model and Comprehensive TaxonomyCode1
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain GraphCode0
SIGN: Scalable Inception Graph Neural NetworksCode1
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
Graph Representation Learning via Ladder Gamma Variational AutoencodersCode0
Gossip and Attend: Context-Sensitive Graph Representation LearningCode0
K-Core based Temporal Graph Convolutional Network for Dynamic GraphsCode1
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—0
Π-nets: Deep Polynomial Neural NetworksCode1
Learning to Hash with Graph Neural Networks for Recommender Systems—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
Dual Graph Representation Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified