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 241–250 of 982 papers

TitleStatusHype
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
About Graph Degeneracy, Representation Learning and ScalabilityCode0
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
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Benchmark Results

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