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 1001–1025 of 1570 papers

TitleStatusHype
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices—0
Machine Learning on Dynamic Graphs: A Survey on Applications—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment—0
Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines—0
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution—0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation—0
Time-varying Graph Learning Under Structured Temporal Priors—0
Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs—0
MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Characterizing the Influence of Topology on Graph Learning Tasks—0
Causal Graph Neural Networks for Wildfire Danger Prediction—0
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation—0
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning—0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
Time-Varying Graph Learning with Constraints on Graph Temporal Variation—0
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs—0
Causal Discovery on Dependent Binary Data—0
MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning—0
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Migrate Demographic Group For Fair GNNs—0
Mining fMRI Dynamics with Parcellation Prior for Brain Disease Diagnosis—0
Time-varying Signals Recovery via Graph Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified