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 1226–1250 of 1570 papers

TitleStatusHype
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
Recognizing Multimodal Entailment—0
Recognizing Predictive Substructures with Subgraph Information Bottleneck—0
Recommending on graphs: a comprehensive review from a data perspective—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning—0
Regularized Primitive Graph Learning for Unified Vector Mapping—0
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment—0
Reinforced Imitative Graph Learning for Mobile User Profiling—0
Active Sampling for Node Attribute Completion on Graphs—0
Relational Graph Learning for Grounded Video Description Generation—0
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery—0
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
Relation-Aware Graph Foundation Model—0
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding—0
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
RepGN:Object Detection with Relational Proposal Graph Network—0
Representing Videos as Discriminative Sub-graphs for Action Recognition—0
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting—0
ResolvNet: A Graph Convolutional Network with multi-scale Consistency—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified