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 451–500 of 982 papers

TitleStatusHype
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Mitigating Relational Bias on Knowledge Graphs—0
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Graph representation learning for street networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Implicit Graphon Neural RepresentationCode1
Application of Graph Neural Networks and graph descriptors for graph classification—0
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Geometry-Complete Perceptron Networks for 3D Molecular GraphsCode1
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
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