Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
A Machine Learning-Based Framework for Discovering Huntington's Disease Stages: Integrating Graph Representation Learning and clustering to Uncover Progression Dynamics in Longitudinal Enroll-HD Dataset
Huntington's disease (HD) is a progressive brain disorder that gradually affects movement, cognitive function, and behavior. Identifying the stage of the disease accurately and consistently is important for understanding…
Graph Representation LearningPAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
Whilst the vulnerability of graph neural networks (GNNs) to adversarial attacks poses a critical threat to graph representation learning, the understanding of the robust generalization behavior remains a fundamental chal…
Graph Representation LearningAdversarial RobustnessGraph ClassificationGeneralizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference. However, the parameterized knowledge encoded within these …
Graph Representation LearningGraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease
International Classification of Diseases (ICD) is a globally recognized coding system that records diagnostic events during each patient encounter, providing a standardized data foundation for various clinical tasks. How…
Graph Representation LearningTemporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction
Predicting Internet round-trip time (RTT) is critical for routing optimization, quality-of-service (QoS) provisioning, and traffic engineering, yet remains challenging due to long-term temporal dependencies, evolving rou…
Graph Representation LearningLink PredictionGraph LearningT-GINEE: A Tensor-Based Multilayer Graph Representation Learning
Traditional network analysis focuses on single-layer networks, real-world systems often form multilayer networks with multiple relationship types. However, existing methods typically fail to capture complex inter-layer d…
Graph Representation Learning'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning
Dynamic graph neural networks (DGNNs) that operate on snapshot sequences typically fall into one of two categories. \emph{Temporal-first} approaches build per-node temporal embeddings and only afterwards perform spatial …
Graph Representation LearningLink PredictionClustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-based graph learning through the princip…
Graph Representation LearningGraph LearningDistance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis
Major depressive disorder (MDD) is a common neuropsychiatric condition whose accurate diagnosis from resting-state functional magnetic resonance imaging (rs-fMRI) remains difficult. Dynamic functional connectivity (DFC) …
Graph Representation LearningContrastive LearningUltra-High-Definition Image Quality Assessment via Graph Representation Learning
Blind image quality assessment (BIQA) for ultrahighdefinition (UHD) images remains challenging because native-resolution inference is computationally expensive, whereas aggressive resizing or isolated cropping may suppre…
Graph Representation LearningImage Quality AssessmentTopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
Root cause analysis (RCA) in microservices is challenging due to (i) noisy and heterogeneous multimodal observability (metrics, logs, traces), (ii) cascading failure propagation that amplifies downstream symptoms, and (i…
Graph Representation LearningTest-time AdaptationRank Is Not Capacity: Spectral Occupancy for Latent Graph Models
Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, community detection, and related tasks. Yet a basic design choice, the la…
Graph Representation LearningCommunity DetectionLink PredictionCTQWformer: A CTQW-based Transformer for Graph Classification
Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to capture both global structural dependencies and model the dynamic informatio…
Graph Representation LearningGraph ClassificationGraph LearningGraph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated learning, FFT enables distributed agents to jointly refine a shared…
Graph Representation LearningFederated LearningDiversity Curves for Graph Representation Learning
Graph-level representations are crucial tools for characterising structural differences between graphs. However, comparing graphs with different cardinalities, even when sampled from the same underlying distribution, rem…
Graph Representation LearningOn the Safety of Graph Representation Learning
Graph representation learning (GRL) has evolved from topology-only graph embeddings to task-specific supervised GNNs, and more recently to reusable representations and graph foundation models (GFMs). However, existing ev…
Graph Representation LearningSCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing
Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on $k$-nearest neighbors ($k$NN) or other node-level full search algorithms (FSA) to m…
Graph Representation LearningGraph Neural NetworkA Unified Framework of Hyperbolic Graph Representation Learning Methods
Hyperbolic geometry has emerged as an effective latent space for representing complex networks, owing to its ability to capture hierarchical organization and heterogeneous connectivity patterns using low-dimensional embe…
Graph Representation LearningNode ClassificationLink PredictionCheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning
Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation design alone to define the invariances learned by GCL can be brittle …
Graph Representation LearningContrastive LearningTime-varying Interaction Graph ODE for Dynamic Graph Representation Learning
Graph neural Ordinary Differential Equations (ODE) combine neural ODE with the message passing mechanism of Graph Neural Networks (GNN), providing a continuous-time modeling method for graph representation learning. Howe…
Graph Representation Learning