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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 911–920 of 982 papers

TitleStatusHype
A Gentle Introduction to Deep Learning for GraphsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
Bridging the Gap between Community and Node Representations: Graph Embedding via Community DetectionCode0
Multi-Channel Graph Convolutional Networks—0
3D Hand Pose Estimation via Regularized Graph Representation Learning—0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
On Node Features for Graph Neural Networks—0
Heterogeneous Deep Graph InfomaxCode0
Graph Transformer for Graph-to-Sequence LearningCode0
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Benchmark Results

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