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 576–600 of 1570 papers

TitleStatusHype
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