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 1076–1100 of 1570 papers

TitleStatusHype
SoK: Differential Privacy on Graph-Structured Data—0
PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph LearningCode1
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning—0
Explainability and Graph Learning from Social Interactions—0
A Survey on Deep Graph Generation: Methods and Applications—0
Reinforced Imitative Graph Learning for Mobile User Profiling—0
GRAND+: Scalable Graph Random Neural NetworksCode1
Online Graph Learning from Social Interactions—0
Multi-modal Graph Learning for Disease PredictionCode1
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
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022Code1
Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks—0
Hyperbolic Graph Neural Networks: A Review of Methods and ApplicationsCode1
Distribution Preserving Graph Representation Learning—0
Bayesian Deep Learning for Graphs—0
Deep Graph Learning for Anomalous Citation Detection—0
Exploring Edge Disentanglement for Node Classification—0
Graph Lifelong Learning: A Survey—0
Physics-Informed Graph Learning—0
From Quantum Graph Computing to Quantum Graph Learning: A Survey—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
Generalizing Aggregation Functions in GNNs:High-Capacity GNNs via Nonlinear Neighborhood Aggregators—0
Molecule Generation for Drug Design: a Graph Learning Perspective—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified