SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 976–1000 of 1570 papers

TitleStatusHype
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning—0
Stability and Generalization of lp-Regularized Stochastic Learning for GCN—0
Stability and Generalization of Graph Convolutional Neural Networks—0
Stable Prediction on Graphs with Agnostic Distribution Shift—0
Curvature-based Clustering on Graphs—0
Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data—0
Stars: Tera-Scale Graph Building for Clustering and Graph Learning—0
Supercharging Graph Transformers with Advective Diffusion—0
Steering Graph Neural Networks with Pinning Control—0
Structural Node Embeddings with Homomorphism Counts—0
Structure-Aware Random Fourier Kernel for Graphs—0
Structured Graph Learning for Clustering and Semi-supervised Classification—0
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view—0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling—0
Subgraph Clustering and Atom Learning for Improved Image Classification—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
SVGraph: Learning Semantic Graphs from Instructional Videos—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Synthetic Graph Generation to Benchmark Graph Learning—0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
Data-Augmented Counterfactual Learning for Bundle Recommendation—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
Data-centric Federated Graph Learning with Large Language Models—0
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified