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 1–25 of 1570 papers

TitleStatusHype
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation—0
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Graph World ModelCode1
Federated Learning with Graph-Based Aggregation for Traffic Forecasting—0
Graph Learning—0
GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph LearningCode2
S2FGL: Spatial Spectral Federated Graph LearningCode0
Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning—0
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based AugmentationsCode0
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities—0
Higher-Order Graph DatabasesCode0
Fast and Distributed Equivariant Graph Neural Networks by Virtual Node LearningCode1
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning—0
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Dynamic Graph Condensation—0
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and BenchmarkCode0
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics—0
Collaborative Interest-aware Graph Learning for Group Identification—0
TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure AttacksCode0
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning—0
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Towards Multi-modal Graph Large Language Model—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified