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

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Showing 476–500 of 982 papers

TitleStatusHype
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Federated Graph Representation Learning using Self-Supervision—0
Implications of sparsity and high triangle density for graph representation learning—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Transformers over Directed Acyclic GraphsCode1
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Graph sampling for node embedding—0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
Unifying Graph Contrastive Learning with Flexible Contextual ScopesCode1
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
Uplifting Message Passing Neural Network with Graph Original Information—0
Towards Real-Time Temporal Graph LearningCode0
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Expander Graph PropagationCode1
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation LearningCode1
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
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Benchmark Results

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