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 501–510 of 982 papers

TitleStatusHype
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
Knowledge-enhanced Session-based Recommendation with Temporal Transformer—0
Knowledge Graph Representation Learning using Ordinary Differential Equations—0
Knowledge Probing for Graph Representation Learning—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning—0
Large-scale graph representation learning with very deep GNNs and self-supervision—0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations—0
Show:102550
← PrevPage 51 of 99Next →

Benchmark Results

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