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
Universal Graph Continual Learning—0
Deep Graph Learning for Anomalous Citation Detection—0
A Versatile Graph Learning Approach through LLM-based Agent—0
Unlocking Multi-Modal Potentials for Dynamic Text-Attributed Graph Representation—0
Unrolled Graph Learning for Multi-Agent Collaboration—0
Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration—0
Deep graph learning for semi-supervised classification—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction—0
DEEP GRAPH SPECTRAL EVOLUTION NETWORKS FOR GRAPH TOPOLOGICAL TRANSFORMATION—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
Utilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation—0
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
DEEP GRAPH TREE NETWORKS—0
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries—0
Visual Tracking via Dynamic Graph Learning—0
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval—0
Network Topology Inference from Smooth Signals Under Partial Observability—0
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening—0
Weakly Supervised Graph Clustering—0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified