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 276–300 of 982 papers

TitleStatusHype
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—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
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Graph AI in Medicine—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
Deep Representation Learning For Multimodal Brain Networks—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—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
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—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