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 481–490 of 982 papers

TitleStatusHype
Transformers over Directed Acyclic GraphsCode1
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Graph sampling for node embedding—0
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
Unifying Graph Contrastive Learning with Flexible Contextual ScopesCode1
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
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Benchmark Results

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