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 891–900 of 982 papers

TitleStatusHype
Material Prediction for Design Automation Using Graph Representation LearningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
Memory-based Message Passing: Decoupling the Message for Propogation from DiscriminationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
GL-Coarsener: A Graph representation learning framework to construct coarse grid hierarchy for AMG solversCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised ClassificationCode0
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
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Benchmark Results

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