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 626–650 of 1570 papers

TitleStatusHype
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
Graph Construction with Label Information for Semi-Supervised Learning—0
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction—0
Graph Learning with Distributional Edge Layouts—0
Deep graph learning for semi-supervised classification—0
Graph Learning with Localized Neighborhood Fairness—0
Graph Condensation for Open-World Graph Learning—0
Deep Graph Learning for Anomalous Citation Detection—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph Learning with Loss-Guided Training—0
Graph Classification with 2D Convolutional Neural Networks—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Deep Graph Clustering via Mutual Information Maximization and Mixture Model—0
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks—0
Graph Domain Adaptation: A Generative View—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Graph Federated Learning Based on the Decentralized Framework—0
Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation—0
Graph Force Learning—0
Graph Lifelong Learning: A Survey—0
GmCN: Graph Mask Convolutional Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified