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 1051–1075 of 1570 papers

TitleStatusHype
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—0
Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning—0
Multi-modal Graph Learning for Disease Prediction—0
Bosonic Random Walk Networks for Graph Learning—0
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation—0
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems—0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery—0
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
Multi-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
Multiple Graph Learning for Scalable Multi-view Clustering—0
Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders—0
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems—0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
Multi-Scale Graph Learning for Anti-Sparse Downscaling—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models—0
Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders—0
TorchGT: A Holistic System for Large-scale Graph Transformer Training—0
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms—0
Benchmarking Graph Learning for Drug-Drug Interaction Prediction—0
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified