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 1201–1225 of 1570 papers

TitleStatusHype
Online Graph Learning from Social Interactions—0
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer NetworksCode0
Hybrid Model-based / Data-driven Graph Transform for Image Coding—0
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer—0
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks—0
Distribution Preserving Graph Representation Learning—0
Bayesian Deep Learning for Graphs—0
Exploring Edge Disentanglement for Node Classification—0
Deep Graph Learning for Anomalous Citation Detection—0
Physics-Informed Graph Learning—0
Graph Lifelong Learning: A Survey—0
From Quantum Graph Computing to Quantum Graph Learning: A Survey—0
Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
Molecule Generation for Drug Design: a Graph Learning Perspective—0
Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework—0
When Does A Spectral Graph Neural Network Fail in Node Classification?—0
Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks—0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Graph Neural Networks for Graphs with Heterophily: A Survey—0
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction—0
Robust Time-Varying Graph Signal Recovery for Dynamic Physical Sensor Network Data—0
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction—0
Robust Graph Representation Learning for Local Corruption RecoveryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified