Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning
Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal graph neural networks (GNNs) focus on al…
Graph structure learningRepresentation LearningNode ClassificationLink PredictionSLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning
Semi-supervised learning (SSL) on data streams is challenging due to the continuous evolution of high-volume data and the scarcity of labels. Existing methods are limited in leveraging the intrinsic relationships among s…
Graph structure 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 LearningTopology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed graph. In real applications, graph topology is often imperfect: nois…
Graph structure learningGraph LearningIs Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational Deep Learning (RDL) addresses this problem by modeling RDBs as graph…
Graph structure learningInformative Graph Structure Learning
The quality of graph-structured data is fundamental to the success of modern graph analysis techniques such as Graph Neural Networks (GNNs). However, real-world graph data is often suboptimal, suffering from issues such …
Graph structure learningBilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain
Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph structure, with the resulting performance gain attributed to the rewired a…
Graph structure learningNode ClassificationMulti-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation
In recent years, the use of edge information provided by knowledge graphs together with the advantages of higher-order connectivity in graph neural networks for recommendation systems has become an important research dir…
Graph structure learningRecommendation SystemsContrastive LearningKnowledge GraphsRobust 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 LearningEAD-Net: Emotion-Aware Talking Head Generation with Spatial Refinement and Temporal Coherence
Emotionally talking head video generation aims to generate expressive portrait videos with accurate lip synchronization and emotional facial expressions. Current methods rely on simple emotional labels, leading to insuff…
Graph structure learningComputational EfficiencyTalking Head GenerationVideo GenerationGraph Topology Information Enhanced Heterogeneous Graph Representation Learning
Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph S…
Graph Representation LearningGraph structure learningLearning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection
Graph signal processing (GSP) is becoming a major tool in biomedical signal and image analysis. In most GSP techniques, graph structures and edge weights have been typically set via statistical and computational methods.…
Graph structure learningData AugmentationGraph LearningCausal Direction from Convergence Time: Faster Training in the True Causal Direction
We introduce Causal Computational Asymmetry (CCA), a principle for causal direction identification based on optimization dynamics in which one neural network is trained to predict $Y$ from $X$ and another to predict $X$ …
Graph structure learningGraph Autoencoder for Process Monitoring
To improve the reliability and interpretability of industrial process monitoring, this article proposes a Causal Graph Spatial-Temporal Autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a …
Graph structure learningWeighted Graph Clustering via Scale Contraction and Graph Structure Learning
Graph clustering aims to partition nodes into distinct clusters based on their similarity, thereby revealing relationships among nodes. Nevertheless, most existing methods do not fully utilize these edge weights. Leverag…
Graph structure learningGraph ClusteringDynamic Graph Structure Learning via Resistance Curvature Flow
Geometric Representation Learning (GRL) aims to approximate the non-Euclidean topology of high-dimensional data through discrete graph structures, grounded in the manifold hypothesis. However, traditional static graph co…
Graph structure learningRepresentation LearningMetric LearningGADPN: Graph Adaptive Denoising and Perturbation Networks via Singular Value Decomposition
While Graph Neural Networks (GNNs) excel on graph-structured data, their performance is fundamentally limited by the quality of the observed graph, which often contains noise, missing links, or structural properties misa…
Graph structure learningTask-driven Heterophilic Graph Structure Learning
Graph neural networks (GNNs) often struggle to learn discriminative node representations for heterophilic graphs, where connected nodes tend to have dissimilar labels and feature similarity provides weak structural cues.…
Graph structure learningHyperbolic Continuous Structural Entropy for Hierarchical Clustering
Hierarchical clustering is a fundamental machine-learning technique for grouping data points into dendrograms. However, existing hierarchical clustering methods encounter two primary challenges: 1) Most methods specify d…
Graph structure learningToward Robust Signed Graph Learning through Joint Input-Target Denoising
Signed Graph Neural Networks (SGNNs) are widely adopted to analyze complex patterns in signed graphs with both positive and negative links. Given the noisy nature of real-world connections, the robustness of SGNN has als…
Graph Representation LearningGraph structure learningInformation ExtractionGraph Learning