Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Random-Set Graph Neural Networks
Uncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite their predictive capabilities being ever useful across industrial work…
Autonomous DrivingGraph LearningFrom Message-Passing to Linearized Graph Sequence Models
Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of modern deep learning architectures in other domains, particularly sequenc…
Graph LearningTowards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to support various network applications. However, real-world graphs are oft…
Graph LearningUFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing CGL methods largely assume clean supervi…
Graph LearningOne-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
Neural Flows efficiently model irregular multivariate time series by directly learning ODE solution trajectories with neural networks, bypassing step-by-step numerical solvers. Despite their efficiency, many existing app…
Time Series ClassificationGraph LearningPrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hundreds of thousand…
Continual LearningKnowledge GraphsGraph LearningCTQWformer: 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 LearningDCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Large Language Models (LLMs) has further enhanced semantic understanding …
Recommendation SystemsContrastive LearningKnowledge GraphsGraph LearningGraphReAct: Reasoning and Acting for Multi-step Graph Inference
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to graph learning remains underexplored. Graph…
Semantic RetrievalGraph LearningBelief or Circuitry? Causal Evidence for In-Context Graph Learning
How do LLMs learn in-context? Is it by pattern-matching recent tokens, or by inferring latent structure? We probe this question using a toy graph random-walk across two competing graph structures. This task's answer is, …
Graph LearningFull-Spectrum Graph Neural Networks: Expressive and Scalable
It is well established that spectral graph neural networks (GNNs) can universally approximate node signals; however, their expressive power remains bounded by the 1-dimensional Weisfeiler-Lehman test, which is mirrored i…
Graph LearningFrom Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
Graph Neural Networks (GNNs) achieve high performance but can be opaque to humans, making it difficult to understand and compare the many proposed architectures. While existing explainability methods attribute individual…
Graph LearningHeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
Many real-world heterogeneous graphs exhibit pronounced heterophily, where connected nodes often have dissimilar labels or play different semantic roles. In such settings, standard heterogeneous graph neural networks tha…
Representation LearningGraph LearningBridging Input Feature Spaces Towards Graph Foundation Models
Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but also in value ranges and dimensionality. This misalignment prevents…
Graph LearningStable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
Graph unlearning remains a critical technique for supporting privacy-preserving and sustainable multimodal graph learning. However, we observe that existing unlearning strategies tend to apply uniform parameter selection…
Graph Neural NetworkGraph LearningGeneralized Category Discovery in Federated Graph Learning
Federated Graph Learning (FGL) enables collaborative learning over distributed graph data, yet existing approaches largely rely on a closed-world assumption, limiting their applicability in dynamic environments where nov…
Graph LearningPosition: How can Graphs Help Large Language Models?
With the rapid advancement of large language models (LLMs), classic graph learning tasks have greatly benefited from LLMs, including improved encoding of textual features, more efficient construction of graphs from text,…
Knowledge GraphsGraph LearningMeta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning
A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these directly affect adsorption energies, reaction pathways, and catalytic perfor…
Graph LearningRethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
Multi-label node classification (MLNC) has recently been addressed by increasingly complex label-aware designs that explicitly model node-label interactions and inter-label dependencies.However, it remains unclear whethe…
Node ClassificationGraph LearningRobust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach
Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes of different types and labels interact in diverse and often non-homophilous ways. Despite …
Graph structure learningRepresentation LearningGraph Learning