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 651–660 of 982 papers

TitleStatusHype
ARIEL: Adversarial Graph Contrastive LearningCode0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
Towards Graph Representation Learning Based Surgical Workflow AnticipationCode0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
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Benchmark Results

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