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

Showing 211220 of 982 papers

TitleStatusHype
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Deep Graph Contrastive Representation LearningCode1
Understanding Negative Sampling in Graph Representation LearningCode1
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
Machine Learning on Graphs: A Model and Comprehensive TaxonomyCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
K-Core based Temporal Graph Convolutional Network for Dynamic GraphsCode1
Π-nets: Deep Polynomial Neural NetworksCode1
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural NetworkCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
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Benchmark Results

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