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 141150 of 982 papers

TitleStatusHype
Neural Approximation of Graph Topological FeaturesCode1
How Expressive are Transformers in Spectral Domain for Graphs?Code1
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
RepBin: Constraint-based Graph Representation Learning for Metagenomic BinningCode1
Graph Neural Networks with Adaptive ResidualCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Implicit SVD for Graph Representation LearningCode1
Hierarchical Heterogeneous Graph Representation Learning for Short Text ClassificationCode1
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Benchmark Results

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