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 1201–1250 of 1570 papers

TitleStatusHype
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation—0
A Survey on Optimal Transport for Machine Learning: Theory and Applications—0
Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videos—0
PROMPT: Parallel Iterative Algorithm for _p norm linear regression via Majorization Minimization with an application to semi-supervised graph learning—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property—0
Psycholinguistic Tripartite Graph Network for Personality Detection—0
Towards Private Learning on Decentralized Graphs with Local Differential Privacy—0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks—0
PuzzleNet: Scene Text Detection by Segment Context Graph Learning—0
A Survey on Kolmogorov-Arnold Network—0
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval—0
Quantum Graph Learning: Frontiers and Outlook—0
Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity—0
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources—0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
A Survey on Deep Graph Generation: Methods and Applications—0
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling—0
Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network—0
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors—0
Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients—0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives—0
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
Recognizing Multimodal Entailment—0
Recognizing Predictive Substructures with Subgraph Information Bottleneck—0
Recommending on graphs: a comprehensive review from a data perspective—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
Regularized Primitive Graph Learning for Unified Vector Mapping—0
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment—0
Reinforced Imitative Graph Learning for Mobile User Profiling—0
Active Sampling for Node Attribute Completion on Graphs—0
Relational Graph Learning for Grounded Video Description Generation—0
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery—0
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
Relation-Aware Graph Foundation Model—0
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding—0
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
RepGN:Object Detection with Relational Proposal Graph Network—0
Representing Videos as Discriminative Sub-graphs for Action Recognition—0
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting—0
ResolvNet: A Graph Convolutional Network with multi-scale Consistency—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified