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 526–550 of 1570 papers

TitleStatusHype
A novel hybrid time-varying graph neural network for traffic flow forecasting—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Accurately Solving Rod Dynamics with Graph Learning—0
Dynamical And-Or Graph Learning for Object Shape Modeling and Detection—0
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks—0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—0
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
Nonlinear Causal Discovery for Grouped Data—0
Graph Classification with 2D Convolutional Neural Networks—0
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
Learning Multi-layer Graphs and a Common Representation for Clustering—0
Dual Space Graph Contrastive Learning—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—0
Graph Agreement Models for Semi-Supervised Learning—0
Bosonic Random Walk Networks for Graph Learning—0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified