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 426–450 of 982 papers

TitleStatusHype
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Graph Representation Learning for Interactive Biomolecule Systems—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
FMGNN: Fused Manifold Graph Neural Network—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Graph Representation Learning for Spatial Image Steganalysis—0
Graph representation learning for street networks—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Graph Representation Learning Towards Patents Network Analysis—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Discriminative Graph Autoencoder—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Graph Representation Learning with Individualization and Refinement—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Graph Self-Contrast Representation Learning—0
Few-Shot Learning on Graphs—0
Show:102550
← PrevPage 18 of 40Next →

Benchmark Results

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