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 1226–1250 of 1570 papers

TitleStatusHype
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals—0
Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs—0
A Higher-Order Semantic Dependency ParserCode0
Balanced Graph Structure Learning for Multivariate Time Series ForecastingCode0
Towards Private Learning on Decentralized Graphs with Local Differential Privacy—0
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
Identifying critical nodes in complex networks by graph representation learning—0
Dual Space Graph Contrastive Learning—0
Euclidean geometry meets graph, a geometric deep learning perspective—0
From Molecular Dynamics to MeshGraphNets—0
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach—0
Fine-grained Graph Learning for Multi-view Subspace ClusteringCode0
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet ConvolutionCode0
Representing Videos as Discriminative Sub-graphs for Action Recognition—0
Stratified Graph SpectraCode0
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention NetworksCode0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
BScNets: Block Simplicial Complex Neural NetworksCode0
Distributed Graph Learning with Smooth Data Priors—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified