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 971980 of 982 papers

TitleStatusHype
ARIEL: Adversarial Graph Contrastive LearningCode0
Towards Expressive Graph RepresentationCode0
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain GraphCode0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
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
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Benchmark Results

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