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 1126–1150 of 1570 papers

TitleStatusHype
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding—0
Gradual Weisfeiler-Leman: Slow and Steady Wins the RaceCode0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
Flashlight: Scalable Link Prediction with Effective Decoders—0
Graph Contrastive Learning with Cross-view Reconstruction—0
SPGP: Structure Prototype Guided Graph Pooling—0
Graph Neural Modeling of Network Flows—0
Multimodal Graph Learning for Deepfake Detection—0
Multimodal learning with graphs—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Implicit Session Contexts for Next-Item RecommendationsCode0
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps—0
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
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
SGAT: Simplicial Graph Attention NetworkCode0
Privacy and Transparency in Graph Machine Learning: A Unified Perspective—0
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
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRankCode0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Wasserstein multivariate auto-regressive models for modeling distributional time seriesCode0
Enhanced graph-learning schemes driven by similar distributions of motifsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified