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 1501–1525 of 1570 papers

TitleStatusHype
Semi-Supervised Learning With Graph Learning-Convolutional NetworksCode0
Exploiting Edge Features for Graph Neural Networks—0
Graph Learning Network: A Structure Learning AlgorithmCode0
Provably Powerful Graph NetworksCode0
Clustering with Similarity Preserving—0
Stability and Generalization of Graph Convolutional Neural Networks—0
Graph Transformer—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
Robust Graph Data Learning via Latent Graph Convolutional Representation—0
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification—0
A Unified Framework for Structured Graph Learning via Spectral ConstraintsCode0
RepGN:Object Detection with Relational Proposal Graph Network—0
Semi-Supervised Graph Classification: A Hierarchical Graph PerspectiveCode0
Graph Learning over Partially Observed Diffusion Networks: Role of Degree Concentration—0
Learning Context Graph for Person Search—0
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddingsCode0
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation—0
Low-rank Kernel Learning for Graph-based Clustering—0
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling—0
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few LabelsCode0
Collaborative Similarity Embedding for Recommender SystemsCode0
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User PreferencesCode0
Graph-RISE: Graph-Regularized Image Semantic Embedding—0
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty—0
Algebraic graph learning of protein-ligand binding affinity—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified