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

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Showing 776–800 of 982 papers

TitleStatusHype
Graph Representation Learning Network via Adaptive SamplingCode0
Graph Representation Learning for Road Type ClassificationCode0
Towards Real-Time Temporal Graph LearningCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label ClassificationCode0
Hyperbolic Neural NetworksCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
Graph Representation Learning Beyond Node and HomophilyCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
Calibrating and Improving Graph Contrastive LearningCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security PoliciesCode0
Graph Representation Learning: A SurveyCode0
Transformers are efficient hierarchical chemical graph learnersCode0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
Product Manifold Representations for Learning on Biological PathwaysCode0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural NetworksCode0
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Benchmark Results

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