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 676–700 of 982 papers

TitleStatusHype
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
FMGNN: Fused Manifold Graph Neural Network—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning—0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—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
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
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
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Benchmark Results

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