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 1026–1050 of 1570 papers

TitleStatusHype
Deconvolutional Networks on Graph Data—0
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems—0
TorchGT: A Holistic System for Large-scale Graph Transformer Training—0
Decoupling feature propagation from the design of graph auto-encoders—0
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach—0
Toward General and Robust LLM-enhanced Text-attributed Graph Learning—0
Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach—0
Towards Federated Graph Learning in One-shot Communication—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
Towards a Taxonomy of Graph Learning Datasets—0
Towards Data-centric Machine Learning on Directed Graphs: a Survey—0
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement—0
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—0
Towards Federated Graph Learning for Collaborative Financial Crimes Detection—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings—0
Towards Graph Foundation Models: A Survey and Beyond—0
Towards joint graph learning and sampling set selection from data—0
Towards Multi-modal Graph Large Language Model—0
Towards Private Learning on Decentralized Graphs with Local Differential Privacy—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
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified