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 876–900 of 1570 papers

TitleStatusHype
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning—0
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRICode0
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction—0
Mobility-Induced Graph Learning for WiFi Positioning—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
Dirichlet Active Learning—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Improving Collaborative Filtering Recommendation via Graph Learning—0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Certified Defense on the Fairness of Graph Neural NetworksCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental LearningCode0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Multimodal Graph Learning for Modeling Emerging Pandemics with Big DataCode0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified