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 861–870 of 982 papers

TitleStatusHype
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Understanding microbiome dynamics via interpretable graph representation learningCode0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Residual2Vec: Debiasing graph embedding with random graphsCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Connector 0.5: A unified framework for graph representation learningCode0
LightGCN: Evaluated and EnhancedCode0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
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Benchmark Results

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