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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 982 papers

TitleStatusHype
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
div2vec: Diversity-Emphasized Node Embedding—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Distribution Preserving Graph Representation Learning—0
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure—0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
Graph Anomaly Detection in Time Series: A Survey—0
Graph Contrastive Learning with Generative Adversarial Network—0
Graph Learning with Localized Neighborhood Fairness—0
Disentangled Generative Graph Representation Learning—0
Discriminative Graph Autoencoder—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
Directional diffusion models for graph representation learning—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
BCDR: Betweenness Centrality-based Distance Resampling for Graph Shortest Distance Embedding—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
Graph AI in Medicine—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Differential Encoding for Improved Representation Learning over Graphs—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
DeepTrax: Embedding Graphs of Financial Transactions—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
Deep Representation Learning For Multimodal Brain Networks—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Deep Prompt Tuning for Graph Transformers—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
GraLSP: Graph Neural Networks with Local Structural Patterns—0
Deep Multi-attribute Graph Representation Learning on Protein Structures—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Deep Learning on Graphs for Natural Language Processing—0
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Benchmark Results

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