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 1201–1225 of 1570 papers

TitleStatusHype
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
PROMPT: Parallel Iterative Algorithm for _p norm linear regression via Majorization Minimization with an application to semi-supervised graph learning—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property—0
Psycholinguistic Tripartite Graph Network for Personality Detection—0
Towards Private Learning on Decentralized Graphs with Local Differential Privacy—0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks—0
PuzzleNet: Scene Text Detection by Segment Context Graph Learning—0
A Survey on Kolmogorov-Arnold Network—0
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval—0
Quantum Graph Learning: Frontiers and Outlook—0
Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
A Survey on Deep Graph Generation: Methods and Applications—0
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling—0
Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network—0
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors—0
Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients—0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives—0
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified