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 801–825 of 1570 papers

TitleStatusHype
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
Clustering with Similarity Preserving—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
CNN2GNN: How to Bridge CNN with GNN—0
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
Optimal Graph Learning With Partial Tags and Multiple Features for Image and Video Annotation—0
Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs—0
Optimizing k in kNN Graphs with Graph Learning Perspective—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations—0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
P3-Distributed Deep Graph Learning at Scale—0
PANDORA: Deep graph learning based COVID-19 infection risk level forecasting—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
Collaborative Interest-aware Graph Learning for Group Identification—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified