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 351–375 of 982 papers

TitleStatusHype
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
Creating generalizable downstream graph models with random projections—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
A Survey on Spectral Graph Neural Networks—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
GraLSP: Graph Neural Networks with Local Structural Patterns—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Graph Representation learning for Audio & Music genre Classification—0
Graph Representation Learning for Infrared and Visible Image Fusion—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
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Benchmark Results

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