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 621–630 of 982 papers

TitleStatusHype
Federated Graph Representation Learning using Self-Supervision—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Graph sampling for node embedding—0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
Towards Real-Time Temporal Graph LearningCode0
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Benchmark Results

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