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 526–550 of 1570 papers

TitleStatusHype
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining—0
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph LearningCode0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning—0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning—0
FedGIG: Graph Inversion from Gradient in Federated Learning—0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges—0
THeGCN: Temporal Heterophilic Graph Convolutional Network—0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks—0
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and TasksCode0
SPGL: Enhancing Session-based Recommendation with Single Positive Graph LearningCode0
Towards joint graph learning and sampling set selection from data—0
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse DomainsCode0
MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced InterpretationCode0
GLL: A Differentiable Graph Learning Layer for Neural NetworksCode0
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural NetworksCode0
My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis—0
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings—0
AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks—0
Revisiting the Necessity of Graph Learning and Common Graph Benchmarks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified