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 1051–1075 of 1570 papers

TitleStatusHype
Unseen Anomaly Detection on Networks via Multi-Hypersphere LearningCode0
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation—0
DOTIN: Dropping Task-Irrelevant Nodes for GNNsCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization—0
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
Graph neural networks and attention-based CNN-LSTM for protein classificationCode1
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image SegmentationCode1
Joint Multi-view Unsupervised Feature Selection and Graph LearningCode0
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning—0
Expressiveness and Approximation Properties of Graph Neural Networks—0
Entailment Graph Learning with Textual Entailment and Soft TransitivityCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
CGC: Contrastive Graph Clustering for Community Detection and TrackingCode0
Synthetic Graph Generation to Benchmark Graph Learning—0
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected GraphsCode1
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural NetworksCode1
Contrastive Graph Learning for Population-based fMRI ClassificationCode1
Semi-Supervised Graph Learning Meets Dimensionality ReductionCode0
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding AffinityCode1
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey—0
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal TransportCode1
Series Photo Selection via Multi-view Graph Learning—0
Graph Augmentation LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified