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 376–400 of 982 papers

TitleStatusHype
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Towards Better Graph Representation Learning with Parameterized Decomposition & FilteringCode1
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
Hierarchical Transformer for Scalable Graph Learning—0
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningCode2
Connector 0.5: A unified framework for graph representation learningCode0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
What Do GNNs Actually Learn? Towards Understanding their RepresentationsCode0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
A Comprehensive Survey on Deep Graph Representation Learning—0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Class-Imbalanced Learning on Graphs: A SurveyCode1
Graph Representation Learning for Interactive Biomolecule Systems—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
FMGNN: Fused Manifold Graph Neural Network—0
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
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Benchmark Results

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