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 176–200 of 1570 papers

TitleStatusHype
CCGL: Contrastive Cascade Graph LearningCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
Fisher Information Embedding for Node and Graph LearningCode1
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022Code1
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language ModelsCode1
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement PredictionCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN ExpressivenessCode1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
Generative Contrastive Graph Learning for RecommendationCode1
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"Code1
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New BenchmarkCode1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic NetworksCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Graph-based Active Learning for Semi-supervised Classification of SAR DataCode1
Covariant Compositional Networks For Learning GraphsCode1
Graph-based, Self-Supervised Program Repair from Diagnostic FeedbackCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Graph Convolutional Networks for Graphs Containing Missing FeaturesCode1
Uncertainty-based graph convolutional networks for organ segmentation refinementCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified