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 851–900 of 1570 papers

TitleStatusHype
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
A Topology-aware Graph Coarsening Framework for Continual Graph Learning—0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNsCode0
Joint Signal Recovery and Graph Learning from Incomplete Time-Series—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
Diffusion Maps for Signal Filtering in Graph Learning—0
Dynamic Frequency Domain Graph Convolutional Network for Traffic ForecastingCode0
Multi-level graph learning for audio event classification and human-perceived annoyance rating predictionCode0
Robust Graph Neural Network based on Graph DenoisingCode0
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—0
Learning High-Dimensional Differential Graphs From Multi-Attribute Data—0
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting—0
PerCNet: Periodic Complete Representation for Crystal Graphs—0
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning—0
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning—0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs—0
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning—0
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRICode0
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction—0
Mobility-Induced Graph Learning for WiFi Positioning—0
A Consistent Diffusion-Based Algorithm for Semi-Supervised Graph Learning—0
Dirichlet Active Learning—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Improving Collaborative Filtering Recommendation via Graph Learning—0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Certified Defense on the Fairness of Graph Neural NetworksCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental LearningCode0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Multimodal Graph Learning for Modeling Emerging Pandemics with Big DataCode0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified