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 1476–1500 of 1570 papers

TitleStatusHype
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks—0
GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation—0
GLMNet: Graph Learning-Matching Networks for Feature Matching—0
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation—0
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition—0
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
Gradual Domain Adaptation for Graph Learning—0
Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems—0
Grale: Designing Networks for Graph Learning—0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—0
GRAND++: Graph Neural Diffusion with A Source Term—0
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps—0
Graph2Graph Learning with Conditional Autoregressive Models—0
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning—0
Graph Agreement Models for Semi-Supervised Learning—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks—0
Dual Space Graph Contrastive Learning—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Spatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified