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 51–60 of 982 papers

TitleStatusHype
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Spectro-Riemannian Graph Neural Networks—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Graph Representation Learning with Diffusion Generative Models—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
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Benchmark Results

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