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 626–650 of 1570 papers

TitleStatusHype
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Improving Collaborative Filtering Recommendation via Graph Learning—0
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Certified Defense on the Fairness of Graph Neural NetworksCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental LearningCode0
A Metadata-Driven Approach to Understand Graph Neural Networks—0
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic GraphsCode1
Topology-aware Debiased Self-supervised Graph Learning for RecommendationCode0
Multimodal Graph Learning for Modeling Emerging Pandemics with Big DataCode0
Efficient Heterogeneous Graph Learning via Random ProjectionCode1
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
GraphGPT: Graph Instruction Tuning for Large Language ModelsCode2
Open-World Lifelong Graph LearningCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified