Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing
Hyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus on first-order or second-order nearest n…
Graph LearningHyperspectral UnmixingRGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data. However, many existing approaches suffer fr…
Abstract generationGraph LearningModality completion+3Data-centric Federated Graph Learning with Large Language Models
In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph model by only transmitting model paramete…
Graph LearningEnhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data
This work addresses the problem of graph learning from data following a Gaussian Graphical Model (GGM) with a time-varying mean. Graphical Lasso (GL), the standard method for estimating sparse precision matrices, assumes…
Bayesian InferenceGraph LearningNaFM: Pre-training a Foundation Model for Small-Molecule Natural Products
Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products resea…
Contrastive LearningDrug DiscoveryGraph LearningNetwork-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
Network-wide Traffic State Estimation (TSE), which aims to infer a complete image of network traffic states with sparsely deployed sensors, plays a vital role in intelligent transportation systems. With the development o…
Graph LearningRepresentation LearningState EstimationGraph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously e…
Document RankingGraph LearningGraph Representation LearningInformation Retrieval+6Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
Graph Anomaly Detection (GAD) plays a vital role in various data mining applications such as e-commerce fraud prevention and malicious user detection. Recently, Graph Neural Network (GNN) based approach has demonstrated …
Anomaly DetectionGraph Anomaly DetectionGraph LearningGraph Neural NetworkA Survey of Cross-domain Graph Learning: Progress and Future Directions
Graph learning plays a vital role in mining and analyzing complex relationships involved in graph data, which is widely used in many real-world applications like transaction networks and communication networks. Foundatio…
Graph LearningSurveyRAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration
This paper presents RAG-KG-IL, a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models (LLMs) by integrating Retrieval-Augmented Generation (RAG) and Knowledge Graphs …
Graph LearningHallucinationIncremental LearningKnowledge Graphs+2SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning
Graph contrastive learning has emerged as a powerful technique for learning graph representations that are robust and discriminative. However, traditional approaches often neglect the critical role of subgraph structures…
Contrastive LearningData AugmentationGraph LearningTransfer LearningHeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning. However, previous GSL methods assume that graph…
Graph LearningGraph MatchingGraph SimilarityGraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs
Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges. To enable efficient training on such large graphs, mini-batch subgraph sampling is commonly used, which …
Graph LearningDistributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Graph neural networks (GNNs) have delivered remarkable results in various fields. However, the rapid increase in the scale of graph data has introduced significant performance bottlenecks for GNN inference. Both computat…
Graph LearningGraph Neural NetworkGraph SamplingFeature Matching Intervention: Leveraging Observational Data for Causal Representation Learning
A major challenge in causal discovery from observational data is the absence of perfect interventions, making it difficult to distinguish causal features from spurious ones. We propose an innovative approach, Feature Mat…
Causal DiscoveryGraph LearningRepresentation LearningTrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization
Urban traffic optimization is critical for improving transportation efficiency and alleviating congestion, particularly in large-scale dynamic networks. Traditional methods, such as Dijkstra's and Floyd's algorithms, pro…
Computational EfficiencyGraph LearningKolmogorov-Arnold NetworksTraffic PredictionFairness and/or Privacy on Social Graphs
Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for bia…
FairnessGraph LearningA Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings
Dynamic interactions between entities are prevalent in domains like social platforms, financial systems, healthcare, and e-commerce. These interactions can be effectively represented as time-evolving graphs, where predic…
Graph LearningRecommendation SystemsTransfer LearningG-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition
Graph Neural Networks (GNNs) have achieved significant success in machine learning, with wide applications in social networks, bioinformatics, knowledge graphs, and other fields. Most research assumes ideal closed-set en…
Anomaly DetectionGraph Anomaly DetectionGraph LearningKnowledge Graphs+2Unlocking Multi-Modal Potentials for Dynamic Text-Attributed Graph Representation
Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal edges alongside rich textual attributes. A prior approach to representing DyTAGs leverages pre-trained language models to…
Graph Learning