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 426–450 of 1570 papers

TitleStatusHype
Learning on Large Graphs using Intersecting CommunitiesCode0
Can Graph Learning Improve Planning in LLM-based Agents?Code2
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection—0
Graph Condensation for Open-World Graph Learning—0
FUGNN: Harmonizing Fairness and Utility in Graph Neural NetworksCode0
Node Identifiers: Compact, Discrete Representations for Efficient Graph LearningCode1
Repeat-Aware Neighbor Sampling for Dynamic Graph LearningCode0
Graph Sparsification via Mixture of GraphsCode1
Learning Geospatial Region Embedding with Heterogeneous Graph—0
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models—0
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model—0
Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Perception-Inspired Graph Convolution for Music Understanding TasksCode1
A Survey of Large Language Models for GraphsCode3
Disttack: Graph Adversarial Attacks Toward Distributed GNN TrainingCode0
Dynamic GNNs for Precise Seizure Detection and Classification from EEG DataCode2
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Federated Graph Condensation with Information Bottleneck Principles—0
Acceleration Algorithms in GNNs: A SurveyCode2
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks—0
SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge GraphsCode1
CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified