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
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
From Molecular Dynamics to MeshGraphNets—0
Euclidean geometry meets graph, a geometric deep learning perspective—0
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach—0
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingCode1
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
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data AugmentationsCode1
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and DirectionsCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Motif Graph Neural NetworkCode1
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention NetworksCode0
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-DesignCode1
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New ClassesCode1
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction—0
Meta Propagation Networks for Graph Few-shot Semi-supervised LearningCode1
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
Scale-Aware Neural Architecture Search for Multivariate Time Series ForecastingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified