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 776–800 of 1570 papers

TitleStatusHype
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Graphs Generalization under Distribution Shifts—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions—0
Mitigating Subpopulation Bias for Fair Network Topology Inference—0
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
Molecular Classification Using Hyperdimensional Graph Classification—0
Graph Unitary Message Passing—0
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs—0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Causal Graph Neural Networks for Wildfire Danger Prediction—0
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations—0
Graph learning methods to extract empathy supporting regions in a naturalistic stimuli fMRI—0
Uncertainty in Graph Neural Networks: A Survey—0
Analysis of Total Variation Minimization for Clustered Federated Learning—0
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning—0
BloomGML: Graph Machine Learning through the Lens of Bilevel OptimizationCode0
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node EmbeddingCode0
DNNLasso: Scalable Graph Learning for Matrix-Variate DataCode0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified