Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
GNN-based Unified Deep Learning
Deep learning models often struggle to maintain generalizability in medical imaging, particularly under domain-fracture scenarios where distribution shifts arise from varying imaging techniques, acquisition protocols, pa…
Graph LearningGraph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
We present a novel approach for graph classification based on tabularizing graph data via new variants of the Weisfeiler-Leman algorithm and then applying methods for tabular data. The variants are obtained by modifying …
Graph ClassificationGraph LearningSYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification
Scarcity of labeled data, especially for rare events, hinders training effective machine learning models. This paper proposes SYNAPSE-G (Synthetic Augmentation for Positive Sampling via Expansion on Graphs), a novel pipe…
Graph LearningDynamic Mixture-of-Experts for Incremental Graph Learning
Graph incremental learning is a learning paradigm that aims to adapt trained models to continuously incremented graphs and data over time without the need for retraining on the full dataset. However, regular graph machin…
Class Incremental LearningGraph LearningExploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
The global spread of misinformation and concerns about content trustworthiness have driven the development of automated fact-checking systems. Since false information often exploits social media dynamics such as "likes" …
Graph LearningGeometry-Aware Spiking Graph Neural Network
Graph Neural Networks (GNNs) have demonstrated impressive capabilities in modeling graph-structured data, while Spiking Neural Networks (SNNs) offer high energy efficiency through sparse, event-driven computation. Howeve…
Representation LearningGraph Neural NetworkLink PredictionGraph LearningFrom Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are common in domains such as citation networks, social platforms, and knowled…
Knowledge GraphsGraph LearningTANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows
We introduce TANGO -- a dynamical systems inspired framework for graph representation learning that governs node feature evolution through a learned energy landscape and its associated descent dynamics. At the core of ou…
Graph Representation LearningGraph Neural NetworkGraph ClassificationGraph LearningscAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering
Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due to the high dimensionality and prevalen…
Contrastive LearningGraph LearningMulti-Stage Knowledge-Distilled VGAE and GAT for Robust Controller-Area-Network Intrusion Detection
The Controller Area Network (CAN) protocol is a standard for in-vehicle communication but remains susceptible to cyber-attacks due to its lack of built-in security. This paper presents a multi-stage intrusion detection f…
Unsupervised Anomaly DetectionNetwork Intrusion DetectionGraph LearningEfficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification
Accurate prediction of placental diseases via whole slide images (WSIs) is critical for preventing severe maternal and fetal complications. However, WSI analysis presents significant computational challenges due to the m…
Computational EfficiencyGraph LearningOnline Continual Graph Learning
Continual Learning (CL) aims to incrementally acquire new knowledge while mitigating catastrophic forgetting. Within this setting, Online Continual Learning (OCL) focuses on updating models promptly and incrementally fro…
Continual LearningGraph LearningFederated Graph Unlearning
The demand for data privacy has led to the development of frameworks like Federated Graph Learning (FGL), which facilitate decentralized model training. However, a significant operational challenge in such systems is adh…
Graph GenerationGraph LearningReaGAN: Node-as-Agent-Reasoning Graph Agentic Network
Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However, such fixed schemes often suffer from t…
Graph LearningStructured Spectral Graph Learning for Anomaly Classification in 3D Chest CT Scans
With the increasing number of CT scan examinations, there is a need for automated methods such as organ segmentation, anomaly detection and report generation to assist radiologists in managing their increasing workload. …
Multi-Label ClassificationAnomaly ClassificationAnomaly DetectionGraph LearningBreaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection
With the rapid expansion of web-based applications and cloud services, malicious JavaScript code continues to pose significant threats to user privacy, system integrity, and enterprise security. But, detecting such threa…
Graph LearningGraph-Augmented Large Language Model Agents: Current Progress and Future Prospects
Autonomous agents based on large language models (LLMs) have demonstrated impressive capabilities in a wide range of applications, including web navigation, software development, and embodied control. While most LLMs are…
Graph LearningDGP: A Dual-Granularity Prompting Framework for Fraud Detection with Graph-Enhanced LLMs
Real-world fraud detection applications benefit from graph learning techniques that jointly exploit node features, often rich in textual data, and graph structural information. Recently, Graph-Enhanced LLMs emerge as a p…
Fraud DetectionGraph LearningLiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection
Detecting driver fatigue is critical for road safety, as drowsy driving remains a leading cause of traffic accidents. Many existing solutions rely on computationally demanding deep learning models, which result in high l…
Facial Landmark DetectionGraph Neural NetworkGraph LearningBuildSTG: A Multi-building Energy Load Forecasting Method using Spatio-Temporal Graph Neural Network
Due to the extensive availability of operation data, data-driven methods show strong capabilities in predicting building energy loads. Buildings with similar features often share energy patterns, reflected by spatial dep…
Graph Neural NetworkGraph Learning