Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning
Robust and interpretable dementia diagnosis from noisy, non-stationary electroencephalography (EEG) is clinically essential yet remains challenging. To this end, we propose SeeGraph, a Sparse-Explanatory dynamic EEG-grap…
Graph LearningBeyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen domains or label spaces. While recent a…
Zero-shot GeneralizationReinforcement LearningGraph LearningBAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop
The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient due to the extensive expert knowledge requ…
Graph Representation LearningGraph Neural NetworkGraph LearningA Theory of Random Graph Shift in Truncated-Spectrum vRKHS
This paper develops a theory of graph classification under domain shift through a random-graph generative lens, where we consider intra-class graphs sharing the same random graph model (RGM) and the domain shift induced …
Graph ClassificationDomain AdaptationGraph LearningHierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
We propose a Hierarchical Multi-scale Knowledge-aware Graph Network (HMKGN) that models multi-scale interactions and spatially hierarchical relationships within whole-slide images (WSIs) for cancer prognostication. Unlik…
Graph LearningModality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation
Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inher…
Multimodal RecommendationGraph LearningProbing Graph Neural Network Activation Patterns Through Graph Topology
Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions. Artifacts of the message passing paradigm in Graph Neural Networks, such as oversmoo…
Graph Neural NetworkGraph LearningLearning Mutual View Information Graph for Adaptive Adversarial Collaborative Perception
Collaborative perception (CP) enables data sharing among connected and autonomous vehicles (CAVs) to enhance driving safety. However, CP systems are vulnerable to adversarial attacks where malicious agents forge false ob…
Autonomous VehiclesGraph LearningPACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework
Online social networks often form opposing echo chambers that reinforce opinion polarization. Under the Friedkin-Johnsen (FJ) model, ModerateInternal (MI) and ModerateExpressed (ME) reduce polarization by neutralizing se…
Reinforcement LearningGraph LearningSpiking Graph Predictive Coding for Reliable OOD Generalization
Graphs provide a powerful basis for modeling Web-based relational data, with expressive GNNs to support the effective learning in dynamic web environments. However, real-world deployment is hindered by pervasive out-of-d…
Graph LearningGLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Understanding disease-gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation and literature review are labour-intensi…
Graph LearningDrug DiscoveryCausal Neighbourhood Learning for Invariant Graph Representations
Graph data often contain noisy and spurious correlations that mask the true causal relationships, which are essential for enabling graph models to make predictions based on the underlying causal structure of the data. De…
Graph LearningOptimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve complex problems involving relationships and c…
Graph LearningProbability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analysis is promising, atlas-based representa…
Graph LearningCOMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models
State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as transformers. Advancing SMMs to graph struct…
Graph Neural NetworkGraph LearningFederated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes
Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundament…
Federated LearningCausal InferenceGraph LearningBeyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a significant barrier to trustworthiness. While self-explainable GNNs attempt to bridge…
Graph LearningDrug DiscoveryIncluding Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models
This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated with each node) that is usually ignored in…
Graph ClusteringGraph LearningEdge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era
Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi…
Representation LearningGraph LearningOPBench: A Graph Benchmark to Combat the Opioid Crisis
The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational solutions. To combat this lethal opioid crisis, graph learning methods …
Graph Learning