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 476–500 of 1570 papers

TitleStatusHype
Adaptive Sparsified Graph Learning Framework for Vessel Behavior Anomalies—0
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks—0
Are Large Language Models In-Context Graph Learners?—0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Incomplete Graph Learning: A Comprehensive SurveyCode0
Utilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning—0
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling—0
Recent Advances in Malware Detection: Graph Learning and Explainability—0
Simple Path Structural Encoding for Graph TransformersCode0
Graph Diffusion Network for Drug-Gene PredictionCode0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
Deep Semantic Graph Learning via LLM based Node Enhancement—0
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems—0
Prompt-Driven Continual Graph LearningCode0
HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion—0
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure PurificationCode0
No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning DatasetsCode0
A Metric for the Balance of Information in Graph Learning—0
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data—0
Gradual Domain Adaptation for Graph Learning—0
ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning—0
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified