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 961970 of 982 papers

TitleStatusHype
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
Representation Learning for Spatial Graphs0
Adversarial Classifier for Imbalanced Problems0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
Discriminative Graph Autoencoder0
SGR: Self-Supervised Spectral Graph Representation Learning0
Multi-Task Graph AutoencodersCode0
Adaptive Sampling Towards Fast Graph Representation LearningCode0
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Benchmark Results

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