Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,012편 · 필터 해제
PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning
Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledge held by other subgraphs may complement a receiver's Local model, but an incompatible transfer can override r…
Graph LearningCone Extended Rayleigh Quotients for Directed Graph Learning: Minimax Spectral Certificates, Sensitivity, and Adaptive Control
Directed graph learning naturally leads to trainable nonsymmetric propagation operators with distinct right and left spectral structures. Building on the two-sided cone Rayleigh framework for generalized pencils \[ B_θ-λ…
Graph LearningRethinking Message Passing as Retrieval for Text-Attributed Graph Learning
Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise multilayer perceptrons (MLPs). Despite its …
Graph LearningWhy Does Graph Learning Fail to Fully Benefit from a Text Teacher?
Graph neural networks (GNNs) are widely used to represent complex interactions and relationships among entities. We investigate a multimodal model that combines two complementary ideas: a self-supervised method that enab…
Graph LearningAre LLM-Enhanced GNNs Privacy-Safe?
Large language models (LLMs) have recently advanced graph neural networks (GNNs) by enriching node representations with semantic information, giving rise to LLM-enhanced GNNs that achieve substantial performance gains. H…
Graph LearningOpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning
Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe externally, and relatively resistant to presentation attacks. However, rel…
Graph LearningFlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space
Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Exi…
Personalized Federated LearningGraph LearningTrojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Models
Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) align graph representations with language semantics to support transferable graph learning. Despite these advantages, the backdoor vulnerability of GFMs on …
Graph LearningMulti-Source Wasserstein Distributionally Robust Graph Learning
Network topology inference from graph signals is central to graph signal processing with applications in neuroscience, sensor, and social networks. In practice, target-domain samples are scarce while heterogeneous source…
Graph LearningLearning Random Geometric Graphs Drawn in Probabilistic Metric Spaces
We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric space. This graph learning works for generic datasets, irrespective of…
Graph LearningDifference-of-Convex Regularization for Graph Learning by Differentiable Programming
Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of the graph Laplacian pseudoinverse. While the Laplacian itself is spar…
Graph LearningLearning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
This work focuses on the problem of learning on temporal graphs, with particular emphasis on the task of clustering: obtaining coarse-grained representations by aggregating information from nodes, edges, and temporal dyn…
Community DetectionGraph LearningNonlinear Laplacians Improve Signed-Directed Graph Learning
While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We int…
Node ClassificationLink PredictionGraph LearningTowards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity
Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains. Federated mult…
Contrastive LearningGraph LearningTopoFormer: Topology Meets Attention for Graph Learning
We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is Topo-Scan, a novel module…
Molecular Property PredictionGraph Representation LearningGraph ClassificationGraph LearningWhat Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for general-purpose graph learning, aiming to learn reusable knowledge that generalizes across diverse graph domains and downstream tasks, redu…
Graph LearningDynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators
Temporal graph learning is commonly organized around the evolution of node states or the encoding of interaction histories. We study an underexplored, operator-centric question: should the graph propagation mechanism its…
Computational EfficiencyGraph LearningFedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clients must learn emerging classes from private multimodal graph streams…
Graph LearningToward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning
Knowledge graph learning provides a powerful framework for representing and inferring structured knowledge, with broad practical applications. However, the scarcity of relation-specific labeled triples per entity hinders…
Graph LearningDoes Graph Compression Preserve Signal Propagation?
Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored. Existing work evaluates compression through downstream task performance or structura…
Graph Learning