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 341–350 of 982 papers

TitleStatusHype
Graph Contrastive Learning with Generative Adversarial Network—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
Neural Architecture RetrievalCode1
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Frameless Graph Knowledge DistillationCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Neural Causal Graph Collaborative FilteringCode0
Show:102550
← PrevPage 35 of 99Next →

Benchmark Results

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