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 221230 of 982 papers

TitleStatusHype
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
When Do Flat Minima Optimizers Work?Code1
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation LearningCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Towards Better Graph Representation Learning with Parameterized Decomposition & FilteringCode1
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Show:102550
← PrevPage 23 of 99Next →

Benchmark Results

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