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Graph Learning
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction
Graph World Model
Federated Learning with Graph-Based Aggregation for Traffic Forecasting
Graph Learning
GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
S2FGL: Spatial Spectral Federated Graph Learning
Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities
Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
Higher-Order Graph Databases
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
Dynamic Graph Condensation
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics
TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks
Collaborative Interest-aware Graph Learning for Group Identification
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning
Towards Multi-modal Graph Large Language Model
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols
H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
HSG-12M: A Large-Scale Spatial Multigraph Dataset
EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments
Masked Language Models are Good Heterogeneous Graph Generalizers
Nonlinear Causal Discovery for Grouped Data
Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos
SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference
Algorithm Unrolling-based Denoising of Multimodal Graph Signals
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
Simple yet Effective Graph Distillation via Clustering
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