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 826–850 of 1570 papers

TitleStatusHype
PatSTEG: Modeling Formation Dynamics of Patent Citation Networks via The Semantic-Topological Evolutionary Graph—0
PerCNet: Periodic Complete Representation for Crystal Graphs—0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training—0
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems—0
Physics-Informed Graph Learning—0
PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels—0
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
Tackling the Local Bias in Federated Graph Learning—0
Pre-demosaic Graph-based Light Field Image Compression—0
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning—0
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification—0
Primitive Graph Learning for Unified Vector Mapping—0
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning—0
Privacy and Transparency in Graph Machine Learning: A Unified Perspective—0
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning—0
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling—0
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints—0
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation—0
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified