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 351–360 of 982 papers

TitleStatusHype
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Controversy Detection: a Text and Graph Neural Network Based Approach—0
Creating generalizable downstream graph models with random projections—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
A Survey on Spectral Graph Neural Networks—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Show:102550
← PrevPage 36 of 99Next →

Benchmark Results

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