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 1126–1150 of 1570 papers

TitleStatusHype
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
A Unified Framework for Optimization-Based Graph Coarsening—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
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning—0
Online Multi-modal Root Cause Analysis—0
Online Network Inference from Graph-Stationary Signals with Hidden Nodes—0
Online Proximal ADMM for Graph Learning from Streaming Smooth Signals—0
Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information—0
On Locality in Graph Learning via Graph Neural Network—0
From Local Structures to Size Generalization in Graph Neural Networks—0
Towards Data-centric Machine Learning on Directed Graphs: a Survey—0
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning—0
On The Effect of Hyperedge Weights On Hypergraph Learning—0
On the Expressivity of Persistent Homology in Graph Learning—0
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method—0
On the Hölder Stability of Multiset and Graph Neural Networks—0
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks—0
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement—0
Visual Tracking via Dynamic Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified