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 976–1000 of 1570 papers

TitleStatusHype
Multimodal learning with graphs—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Implicit Session Contexts for Next-Item RecommendationsCode0
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashingCode1
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps—0
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking GraphCode1
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy—0
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation—0
SGAT: Simplicial Graph Attention NetworkCode0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Privacy and Transparency in Graph Machine Learning: A Unified Perspective—0
SCARA: Scalable Graph Neural Networks with Feature-Oriented OptimizationCode1
Demystifying Graph Convolution with a Simple Concatenation—0
SVGraph: Learning Semantic Graphs from Instructional Videos—0
Unsupervised feature selection method based on iterative similarity graph factorization and clustering by modularityCode0
Learning Long-Term Spatial-Temporal Graphs for Active Speaker DetectionCode1
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRankCode0
Wasserstein multivariate auto-regressive models for modeling distributional time seriesCode0
Enhanced graph-learning schemes driven by similar distributions of motifsCode0
Graph-based Molecular Representation LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified