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 601–625 of 1570 papers

TitleStatusHype
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models—0
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors—0
Online Multi-modal Root Cause Analysis—0
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization—0
Federated Graph Learning for Cross-Domain Recommendation—0
Generalization from Starvation: Hints of Universality in LLM Knowledge Graph Learning—0
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
Multi-Stage Graph Learning for fMRI Analysis to Diagnose Neuro-Developmental Disorders—0
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering—0
How to Make LLMs Strong Node Classifiers?—0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems—0
Graph-Based Representation Learning of Neuronal Dynamics and BehaviorCode0
GUNDAM: Aligning Large Language Models with Graph Understanding—0
One Node Per User: Node-Level Federated Learning for Graph Neural Networks—0
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting—0
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity RecognitionCode0
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks—0
HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified