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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1076–1100 of 1570 papers

TitleStatusHype
Multivariate Relations Aggregation Learning in Social Networks—0
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning—0
Multi-view Graph Convolutional Networks with Differentiable Node Selection—0
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis—0
Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior—0
Multiview Graph Learning with Consensus Graph—0
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization—0
Multi-view Subspace Clustering via Partition Fusion—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis—0
Toward Data-centric Directed Graph Learning: An Entropy-driven Approach—0
Bayesian Deep Learning for Graphs—0
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation—0
Negative Sampling for Contrastive Representation Learning: A Review—0
Neighbor group structure preserving based consensus graph learning for incomplete multi-view clustering—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Network Games Induced Prior for Graph Topology Learning—0
Network Momentum across Asset Classes—0
Toward General and Robust LLM-enhanced Text-attributed Graph Learning—0
Neural Algorithms for Graph Navigation—0
AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning—0
Automated Knowledge Graph Learning in Industrial Processes—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Show:102550
← PrevPage 44 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified