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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 1570 papers

TitleStatusHype
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics—0
Semi-decentralized Federated Ego Graph Learning for Recommendation—0
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction—0
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction—0
Semi-supervised Data Representation via Affinity Graph Learning—0
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models—0
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation—0
Semi-Supervised Hierarchical Graph Classification—0
Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data—0
Series Photo Selection via Multi-view Graph Learning—0
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements—0
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning—0
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation—0
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning—0
SGL: Spectral Graph Learning from Measurements—0
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Cost-Optimal Learning of Causal Graphs—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
Signal Processing over Time-Varying Graphs: A Systematic Review—0
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation—0
SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering—0
Plain Transformers Can be Powerful Graph Learners—0
Coupled Attention Networks for Multivariate Time Series Anomaly Detection—0
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified