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 601–650 of 982 papers

TitleStatusHype
Learning Graph Search Heuristics—0
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
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—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
Application of Graph Neural Networks and graph descriptors for graph classification—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Implications of sparsity and high triangle density for graph representation learning—0
Federated Graph Representation Learning using Self-Supervision—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Graph sampling for node embedding—0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
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
Towards Real-Time Temporal Graph LearningCode0
Uplifting Message Passing Neural Network with Graph Original Information—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Revisiting Embeddings for Graph Neural Networks—0
Cell Attention NetworksCode0
Machine Learning Partners in Criminal Networks—0
Temporal knowledge graph representation learning with local and global evolutionsCode0
A Class-Aware Representation Refinement Framework for Graph Classification—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Robust Causal Graph Representation Learning against Confounding EffectsCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax—0
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Benchmark Results

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