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 1301–1350 of 1570 papers

TitleStatusHype
Deep Generative Models for Subgraph PredictionCode0
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural NetworksCode0
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity RecognitionCode0
Deep Insights into Noisy Pseudo Labeling on Graph DataCode0
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and BenchmarkCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Descriptive Kernel Convolution Network with Improved Random Walk KernelCode0
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary PatternsCode0
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRankCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning ApproachCode0
Distances for Markov Chains, and Their DifferentiationCode0
Distributed-Order Fractional Graph Operating NetworkCode0
Disttack: Graph Adversarial Attacks Toward Distributed GNN TrainingCode0
DNNLasso: Scalable Graph Learning for Matrix-Variate DataCode0
DOTIN: Dropping Task-Irrelevant Nodes for GNNsCode0
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilitiesCode0
Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention NetworksCode0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
Dynamic Frequency Domain Graph Convolutional Network for Traffic ForecastingCode0
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static RelationsCode0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
E-CGL: An Efficient Continual Graph LearnerCode0
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit FieldsCode0
Efficient Graph Laplacian Estimation by Proximal NewtonCode0
Efficient Multi-View Graph Clustering with Local and Global Structure PreservationCode0
Certified Defense on the Fairness of Graph Neural NetworksCode0
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices ApproachCode0
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional NetworksCode0
Enhanced graph-learning schemes driven by similar distributions of motifsCode0
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language ProcessingCode0
Entailment Graph Learning with Textual Entailment and Soft TransitivityCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy EnvironmentsCode0
Expectation-Complete Graph Representations with HomomorphismsCode0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Exploring the Representational Power of Graph AutoencoderCode0
Fair Attribute Completion on Graph with Missing AttributesCode0
FairGT: A Fairness-aware Graph TransformerCode0
Fairness and/or Privacy on Social GraphsCode0
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural NetworksCode0
Federated Continual Graph LearningCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Federated Graph Semantic and Structural LearningCode0
FedGTA: Topology-aware Averaging for Federated Graph LearningCode0
FedSPA: Generalizable Federated Graph Learning under Homophily HeterogeneityCode0
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingCode0
Fine-grained Graph Learning for Multi-view Subspace ClusteringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified