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 676–700 of 1570 papers

TitleStatusHype
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching—0
Deep Graph Learning for Anomalous Citation Detection—0
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Graph Classification with 2D Convolutional Neural Networks—0
Graph Learning for Inverse Landscape Genetics—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm—0
Graph Learning for Planning: The Story Thus Far and Open Challenges—0
Deep Graph Clustering via Mutual Information Maximization and Mixture Model—0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation—0
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
Graph learning in robotics: a survey—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Algebraic graph learning of protein-ligand binding affinity—0
Directional diffusion models for graph representation learning—0
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization—0
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning—0
Graph Learning via Spectral Densification—0
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement—0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
A Unified Framework for Optimization-Based Graph Coarsening—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified