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 221–230 of 982 papers

TitleStatusHype
Graph Representation Learning via Graphical Mutual Information MaximizationCode1
A Gentle Introduction to Deep Learning for GraphsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Learning Semantic-Specific Graph Representation for Multi-Label Image RecognitionCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
How Powerful are Graph Neural Networks?Code1
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs—0
Show:102550
← PrevPage 23 of 99Next →

Benchmark Results

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