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 1501–1525 of 1570 papers

TitleStatusHype
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters—0
Graph Classification with 2D Convolutional Neural Networks—0
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction—0
Graph Condensation for Open-World Graph Learning—0
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution—0
Spectral GNN via Two-dimensional (2-D) Graph Convolution—0
Graph Construction with Label Information for Semi-Supervised Learning—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization—0
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm—0
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification—0
Each Graph is a New Language: Graph Learning with LLMs—0
Spectral Graph Transformer Networks for Brain Surface Parcellation—0
Graph Domain Adaptation: A Generative View—0
Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation—0
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified