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 51–75 of 982 papers

TitleStatusHype
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Spectro-Riemannian Graph Neural Networks—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Graph Representation Learning with Diffusion Generative Models—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Optimizing Supply Chain Networks with the Power of Graph Neural Networks—0
KAN KAN Buff Signed Graph Neural Networks?—0
Data-Driven Self-Supervised Graph Representation LearningCode0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
LASE: Learned Adjacency Spectral EmbeddingsCode0
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation LearningCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
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Benchmark Results

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