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 751–760 of 982 papers

TitleStatusHype
Heterogeneous Temporal Hypergraph Neural Network—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Heterophily-Aware Graph Attention Network—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Hierarchical Prototype Network for Continual Graph Representation Learning—0
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
Show:102550
← PrevPage 76 of 99Next →

Benchmark Results

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