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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 1570 papers

TitleStatusHype
Rethinking Graph Structure Learning in the Era of LLMs—0
AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram ModelCode0
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
Data-centric Federated Graph Learning with Large Language Models—0
NaFM: Pre-training a Foundation Model for Small-Molecule Natural ProductsCode0
Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder ApproachCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph LearningCode0
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
HeGMN: Heterogeneous Graph Matching Network for Learning Graph SimilarityCode0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs—0
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow OptimizationCode0
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning—0
Fairness and/or Privacy on Social GraphsCode0
A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings—0
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition—0
Unlocking Multi-Modal Potentials for Dynamic Text-Attributed Graph Representation—0
ExPath: Towards Explaining Targeted Pathways for Biological Knowledge Bases—0
Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic GraphsCode0
Graph Masked Language Models—0
A Survey of Graph Transformers: Architectures, Theories and Applications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified