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 776–800 of 1570 papers

TitleStatusHype
Node-Centric Graph Learning from Data for Brain State Identification—0
Node Classification With Integrated Reject Option—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
NodeNet: A Graph Regularised Neural Network for Node Classification—0
Node Similarities under Random Projections: Limits and Pathological Cases—0
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model—0
Non-Parametric Graph Learning for Bayesian Graph Neural Networks—0
Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation—0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks—0
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models—0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium—0
One Node Per User: Node-Level Federated Learning for Graph Neural Networks—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering—0
Online Discriminative Graph Learning from Multi-Class Smooth Signals—0
Online Graph Learning from Social Interactions—0
Online Graph Learning in Dynamic Environments—0
Online Graph Learning under Smoothness Priors—0
Online Graph Learning via Time-Vertex Adaptive Filters: From Theory to Cardiac Fibrillation—0
Online Graph Topology Learning from Matrix-valued Time Series—0
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
Online Multi-modal Root Cause Analysis—0
Online Network Inference from Graph-Stationary Signals with Hidden Nodes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified