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 1276–1300 of 1570 papers

TitleStatusHype
GRAND++: Graph Neural Diffusion with A Source Term—0
Interpreting Graph Neural Networks via Unrevealed Causal Learning—0
Graph Information Matters: Understanding Graph Filters from Interaction Probability—0
On Locality in Graph Learning via Graph Neural Network—0
Understanding Graph Learning with Local Intrinsic Dimensionality—0
Robust Graph Data Learning with Latent Graph Convolutional Representation—0
DEEP GRAPH TREE NETWORKS—0
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
Weakly Supervised Graph Clustering—0
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting—0
DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction—0
Dynamic Sequential Graph Learning for Click-Through Rate Prediction—0
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation—0
Blindness to Modality Helps Entailment Graph MiningCode0
Graph Learning for Cognitive Digital Twins in Manufacturing Systems—0
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields—0
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs—0
A Study of Joint Graph Inference and Forecasting—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Joint Graph Learning and Matching for Semantic Feature CorrespondenceCode0
Computing Steiner Trees using Graph Neural Networks—0
Effective and Efficient Graph Learning for Multi-view Clustering—0
P3-Distributed Deep Graph Learning at Scale—0
Recognizing Multimodal Entailment—0
ROD: Reception-aware Online Distillation for Sparse GraphsCode0
Show:102550
← PrevPage 52 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified