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 1101–1125 of 1570 papers

TitleStatusHype
Neural Stochastic Block Model & Scalable Community-Based Graph Learning—0
Toward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach—0
Neuromorphic Imaging and Classification with Graph Learning—0
Never Skip a Batch: Continuous Training of Temporal GNNs via Adaptive Pseudo-Supervision—0
Towards Federated Graph Learning in One-shot Communication—0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks—0
Node-Centric Graph Learning from Data for Brain State Identification—0
Node Classification With Integrated Reject Option—0
Automated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
NodeNet: A Graph Regularised Neural Network for Node Classification—0
Node Similarities under Random Projections: Limits and Pathological Cases—0
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries—0
Towards a Taxonomy of Graph Learning Datasets—0
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model—0
Non-Parametric Graph Learning for Bayesian Graph Neural Networks—0
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks—0
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models—0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution—0
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs—0
One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium—0
One Node Per User: Node-Level Federated Learning for Graph Neural Networks—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified