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
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Differential Encoding for Improved Representation Learning over Graphs—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
Directional diffusion models for graph representation learning—0
Discriminative Graph Autoencoder—0
Disentangled Generative Graph Representation Learning—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
Distribution Preserving Graph Representation Learning—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
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Benchmark Results

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