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

Showing 976982 of 982 papers

TitleStatusHype
VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming SolutionsCode0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming EventsCode0
Towards Graph Representation Learning Based Surgical Workflow AnticipationCode0
On the Initialization of Graph Neural NetworksCode0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
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Benchmark Results

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