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 201–225 of 982 papers

TitleStatusHype
Multi-hop Attention Graph Neural NetworkCode1
Information Obfuscation of Graph Neural NetworksCode1
Sub-graph Contrast for Scalable Self-Supervised Graph Representation LearningCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Graph-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
Second-Order Pooling for Graph Neural NetworksCode1
Towards Deeper Graph Neural NetworksCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Deep Graph Contrastive Representation LearningCode1
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
Understanding Negative Sampling in Graph Representation LearningCode1
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
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
Show:102550
← PrevPage 9 of 40Next →

Benchmark Results

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