Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mapping
Cortical folding exhibits substantial inter-individual variability while preserving stable anatomical landmarks that enable fine-scale characterization of cortical organization. Among these, the three-hinge gyrus (3HG) s…
Graph Representation LearningGeometric MatchingTransferable Graph Condensation from the Causal Perspective
The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduced substantial training challenges. Graph dataset condensation technique…
Graph Representation LearningContrastive LearningHeterogeneity-Aware Knowledge Sharing for Graph Federated Learning
Graph Federated Learning (GFL) enables distributed graph representation learning while protecting the privacy of graph data. However, GFL suffers from heterogeneity arising from diverse node features and structural topol…
Graph Representation LearningFederated LearningCombating Spurious Correlations in Graph Interpretability via Self-Reflection
Interpretable graph learning has recently emerged as a popular research topic in machine learning. The goal is to identify the important nodes and edges of an input graph that are crucial for performing a specific graph …
Graph Representation LearningGraph LearningFrequent subgraph-based persistent homology for graph classification
Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. Howeve…
Graph Representation LearningGraph ClassificationEnergy-Balanced Hyperspherical Graph Representation Learning via Structural Binding and Entropic Dispersion
Graph Representation Learning (GRL) can be fundamentally modeled as a physical process of seeking an energy equilibrium state for a node system on a latent manifold. However, existing Graph Neural Networks (GNNs) often s…
Graph Representation LearningNode ClassificationLink PredictionPatch as Node: Human-Centric Graph Representation Learning for Multimodal Action Recognition
While human action recognition has witnessed notable achievements, multimodal methods fusing RGB and skeleton modalities still suffer from their inherent heterogeneity and fail to fully exploit the complementary potentia…
Graph Representation LearningAction RecognitionSemantic Refinement with LLMs for Graph Representations
Graph-structured data exhibit substantial heterogeneity in where their predictive signals originate: in some domains, node-level semantics dominate, while in others, structural patterns play a central role. This structur…
Graph Representation LearningSemantic SimilarityGraph LearningA Community-Enhanced Graph Representation Model for Link Prediction
Although Graph Neural Networks (GNNs) have become the dominant approach for graph representation learning, their performance on link prediction tasks does not always surpass that of traditional heuristic methods such as …
Graph Representation LearningLink PredictionJensen-Shannon Divergence Message-Passing for Rich-Text Graph Representation Learning
In this paper, we investigate how the widely existing contextual and structural divergence may influence the representation learning in rich-text graphs. To this end, we propose Jensen-Shannon Divergence Message-Passing …
Graph Representation LearningCausal Heterogeneous Graph Learning Method for Chronic Obstructive Pulmonary Disease Prediction
Due to the insufficient diagnosis and treatment capabilities at the grassroots level, there are still deficiencies in the early identification and early warning of acute exacerbation of Chronic obstructive pulmonary dise…
Graph Representation LearningCausal InferenceGraph LearningFairExpand: Individual Fairness on Graphs with Partial Similarity Information
Individual fairness, which requires that similar individuals should be treated similarly by algorithmic systems, has become a central principle in fair machine learning. Individual fairness has garnered traction in graph…
Graph Representation LearningGraph Neural NetworkMachine Learning for Static and Single-Event Dynamic Complex Network Analysis
The primary objective of this thesis is to develop novel algorithmic approaches for Graph Representation Learning of static and single-event dynamic networks. In such a direction, we focus on the family of Latent Space M…
Graph Representation LearningParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the over-smoothing problem in deep GNNs, global…
Graph Representation LearningGraph ClassificationNode ClassificationGraph LearningLightTopoGAT: Enhancing Graph Attention Networks with Topological Features for Efficient Graph Classification
Graph Neural Networks have demonstrated significant success in graph classification tasks, yet they often require substantial computational resources and struggle to capture global graph properties effectively. We introd…
Graph Representation LearningGraph ClassificationGraph Neural NetworkTopologically-Stabilized Graph Neural Networks: Empirical Robustness Across Domains
Graph Neural Networks (GNNs) have become the standard for graph representation learning but remain vulnerable to structural perturbations. We propose a novel framework that integrates persistent homology features with st…
Graph Representation LearningGraph LearningCLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities
We introduce CLARGA, a general-purpose multimodal fusion architecture for multimodal representation learning that works with any number and type of modalities without changing the underlying framework. Given a supervised…
Graph Representation LearningPR-CapsNet: Pseudo-Riemannian Capsule Network with Adaptive Curvature Routing for Graph Learning
Capsule Networks (CapsNets) show exceptional graph representation capacity via dynamic routing and vectorized hierarchical representations, but they model the complex geometries of real\-world graphs poorly by fixed\-cur…
Graph Representation LearningGraph ClassificationGraph LearningHGC-Herd: Efficient Heterogeneous Graph Condensation via Representative Node Herding
Heterogeneous graph neural networks (HGNNs) have demonstrated strong capability in modeling complex semantics across multi-type nodes and relations. However, their scalability to large-scale graphs remains challenging du…
Graph Representation LearningKIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interactio…
Graph Representation LearningGraph Neural NetworkJet Tagging