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 676–700 of 982 papers

TitleStatusHype
DPGNN: Dual-Perception Graph Neural Network for Representation Learning—0
Asymmetric Graph Representation Learning—0
MGC: A Complex-Valued Graph Convolutional Network for Directed GraphsCode0
Residual2Vec: Debiasing graph embedding with random graphsCode0
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Pre-training Molecular Graph Representation with 3D GeometryCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
Cycle Representation Learning for Inductive Relation PredictionCode0
Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems—0
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision LevelsCode0
Graph Representation Learning for Spatial Image Steganalysis—0
Reconstruction for Powerful Graph Representations—0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
SpecTRA: Spectral Transformer for Graph Representation Learning—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
BCDR: Betweenness Centrality-based Distance Resampling for Graph Shortest Distance Embedding—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Scalable Hierarchical Embeddings of Complex Networks—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
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Benchmark Results

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