Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
Terrestrial network limitations drive the integration of non-terrestrial networks (NTNs), notably mega-constellations comprising thousands of low Earth orbit (LEO) satellites. While these satellites act as interconnected…
Graph LearningLLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or l…
Representation LearningSeizure DetectionGraph LearningSparse Graph Learning from Sparse Data via Fiedler Number Maximization
We aim to learn a sparse and connected graph from sparse data, where the number of observations K can be substantially smaller than the signal dimension N for signals x in R^N, and the underlying distribution is unknown.…
Graph LearningCMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existing graph-based methods optimize modality weights jointly with the clas…
Graph LearningLatent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show tha…
Graph LearningReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation
Open-vocabulary scene graph generation (SGG) aims to describe visual scenes with flexible relation phrases beyond a fixed predicate set. Existing methods usually treat annotated triplets as positives and all unannotated …
Scene Graph GenerationGraph LearningImproved large-scale graph learning through ridge spectral sparsification
Graph-based techniques and spectral graph theory have enriched the field of machine learning with a variety of critical advances. A central object in the analysis is the graph Laplacian L, which encodes the structure of …
Graph LearningSpectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction
Graph Neural Networks (GNNs) excel on relational data, but standard benchmarks unrealistically assume the graph is centrally available. In practice, settings such as Federated Graph Learning, distributed systems, and pri…
Node ClassificationLink PredictionGraph LearningLearning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each ne…
Node ClassificationGraph GenerationGraph LearningInductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt GNNs to heterophilic graphs through non-l…
Node ClassificationCausal InferenceGraph LearningLoReC: Rethinking Large Language Models for Graph Data Analysis
The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies, graph learning can benefit from LLMs. H…
Graph LearningLAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS prioritizes maximizing reconstruction quality over communication throughpu…
Graph Neural NetworkGraph LearningHow Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. This paper provides a controlled benchmar…
Graph ClassificationGraph LearningDual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model
Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1) collaborative filtering approaches struggle with cold-start items ow…
Collaborative FilteringGraph LearningEvidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?
Perceptions of intelligence shape how learners evaluate and rely on artificial intelligence (AI) systems. Despite rapid advances in AI capabilities, the impact of sustained exposure to these tools on students' valuation …
Graph LearningText-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation
In electronic health record (EHR) mining, learning high-quality representations of medical concepts (e.g., standardized diagnosis, medication, and procedure codes) is fundamental for downstream clinical prediction. Howev…
Representation LearningGraph LearningTeaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning
For a robot to be called socially intelligent, it must be able to infer users internal states from their current behaviour, predict the users future behaviour, and if required, respond appropriately. In this work, we inv…
Multi-Task LearningDecision MakingGraph LearningDynamic Sheaf Diffusion Networks with Adaptive Local Structure for Heterogeneous Spatio-Temporal Graph Learning
Spatio-temporal processes often exhibit highly heterogeneous and non-intuitive responses to localized disruptions, limiting the effectiveness of conventional message passing approaches in modeling local heterogeneity. We…
Graph Neural NetworkGraph LearningExploring Concept Subspace for Self-explainable Text-Attributed Graph Learning
We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each concept is a meaningful phrase, and predi…
Graph LearningDBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification
Irregular Medical Time Series play a critical role in the clinical domain to better understand the patient's condition. However, inherent irregularity arising from heterogeneous sampling rates, asynchronous observations,…
Time Series ClassificationRepresentation LearningGraph Learning