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 551–600 of 1570 papers

TitleStatusHype
Accurately Solving Physical Systems with Graph Learning—0
Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties—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
Do graph neural network states contain graph properties?—0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
Graph Learning based Generative Design for Resilience of Interdependent Network Systems—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems—0
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
Graph Learning Approaches to Recommender Systems: A Review—0
Distribution Preserving Graph Representation Learning—0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
Distributed Graph Learning with Smooth Data Priors—0
Graph Learning—0
Graph Learning: A Survey—0
Distilling Large Language Models for Text-Attributed Graph Learning—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Benchmarking Graph Learning for Drug-Drug Interaction Prediction—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Analysis of Total Variation Minimization for Clustered Federated Learning—0
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
Disentangled Generative Graph Representation Learning—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Graph Intention Network for Click-through Rate Prediction in Sponsored Search—0
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data—0
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data—0
Bayesian Deep Learning for Graphs—0
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs—0
Dirichlet Active Learning—0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Directional diffusion models for graph representation learning—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World—0
Diffusion Maps for Signal Filtering in Graph Learning—0
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning—0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption—0
A Metric for the Balance of Information in Graph Learning—0
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design—0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified