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

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Showing 76–100 of 1570 papers

TitleStatusHype
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Automating Botnet Detection with Graph Neural NetworksCode1
Covariant Compositional Networks For Learning GraphsCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
Convolutional Neural Networks on Graphs with Chebyshev Approximation, RevisitedCode1
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and DirectionsCode1
AutoGL: A Library for Automated Graph LearningCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Automatic Relation-aware Graph Network ProliferationCode1
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022Code1
Bilinear Scoring Function Search for Knowledge Graph LearningCode1
An Empirical Evaluation of Temporal Graph BenchmarkCode1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"Code1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Uncertainty-based graph convolutional networks for organ segmentation refinementCode1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified