Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
What Does the Brain See? Multiview Neural Representations to Demystify the Brain-Visual Alignment
Zero-shot visual decoding from electroencephalography (EEG) aims to infer visual semantics from non-invasive neural recordings, but remains challenging due to the low signal-to-noise ratio, non-stationarity, and limited …
Representation LearningContrastive LearningGraph LearningHierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level. Entities at each level can be connected by edges reflecting inherent cor…
Graph LearningTowards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
Federated Graph Learning facilitates collaborative graph modeling across distributed clients while preserving data privacy. However, real-world data categories frequently exhibit long-tailed distributions. Such statistic…
Graph LearningLearning Graphs through Continuous Information Entropy Fields
Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This paper proposes a new explanatory framework for graph learning, wher…
Graph ClassificationNode ClassificationGraph LearningPromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic graphs (DTDGs). Existing methods show impressive advances in capturin…
Graph LearningBridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogeneity and representation degradation during graph learning. Real-worl…
Graph Representation LearningGraph ClusteringGraph LearningEnhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transferability across different datasets. In contrast, language models (LM…
Graph LearningAdaptive Recurrent Message Passing for Test Time Computing on Graphs
Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream tasks. However, extending this paradigm to graph learning remains ch…
Graph LearningPrivacy-Preserving Federated Temporal Graph Learning with Digital Twin--Guided Adaptive Deception for Cyber-Resilient IoMT
The rapid proliferation of IoT and IoMT devices introduces critical cybersecurity vulnerabilities in healthcare and industrial environments where resource-constrained devices operate under strict latency and data-privacy…
Graph LearningTowards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation
Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints. This study presents the first exploratory application of graph-bas…
Graph Neural NetworkLink PredictionGraph LearningTowards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance. Client-level imbalance occurs when cer…
Graph LearningGeometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification
Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual differences between benign and malignant cases. Ex…
Skin Lesion ClassificationSkin Cancer ClassificationMultimodal ReasoningGraph LearningRankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet existing work addresses each in isolation. …
Representation LearningGraph LearningTCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction
Trust prediction infers latent user-user trust relations and provides important support for social recommendation, fake-review and manipulation detection, and risk identification. Graph neural networks have become a prom…
Graph LearningSpectral Sparsification of Laplacian-Constrained Gaussian and Hüsler-Reiss Graphical Models
Graph Laplacians encode graph structures in matrix form, and thus facilitate the application of linear algebra to graph theory. In statistics, two related families of probabilistic graphical models can be parameterized b…
Graph structure learningGraph LearningCIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
Accurate traffic flow prediction remains challenging in cross-city, data-scarce scenarios where limited historical data hinders model generalisation. The chaotic nature of traffic dynamics, complex spatio-temporal depend…
Few-Shot LearningTransfer LearningGraph LearningSMGFM: Spectral Multimodal Graph Pretraining for Multimodal-Attributed Graphs
Multimodal-attributed graphs (MAGs) couple graph topology with node semantics from text, images, and other modalities. Traditional graph learning contextualizes node semantics by coupling topology with node features. How…
Graph LearningBeyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the…
Graph LearningGlobally Localizing Lunar Rover in Pixels via Graph Alignment
Precise rover localization is a prerequisite for autonomous lunar exploration, yet the absence of Global Navigation Satellite System (GNSS) signals and the cumulative drift of local localization methods severely constrai…
Zero-shot GeneralizationSemantic SegmentationGraph LearningGraph MatchingIntention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases. Yet in-possession phases are rarely directly observable and are shaped by evolvi…
Graph Learning