Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields. These models necessitate effective uncertainty …
Decision MakingGraph LearningSurveyUncertainty QuantificationSPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. GNNs effectively implement node representation learning through neighborhood aggregation a…
Graph ClassificationGraph LearningRepresentation LearningCKGConv: General Graph Convolution with Continuous Kernels
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of c…
Graph ClassificationGraph LearningGraph RegressionNode ClassificationGrasper: A Generalist Pursuer for Pursuit-Evasion Problems
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonstrated the effectiveness of the pre-train…
Graph LearningGraph Neural NetworkGraph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching
In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning l…
ClassificationClusteringGraph LearningNode ClassificationNode Similarities under Random Projections: Limits and Pathological Cases
Random Projections have been widely used to generate embeddings for various graph learning tasks due to their computational efficiency. The majority of applications have been justified through the Johnson-Lindenstrauss L…
Computational EfficiencyGraph LearningLEMMAPolynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
Spectral graph neural networks are proposed to harness spectral information inherent in graph-structured data through the application of polynomial-defined graph filters, recently achieving notable success in graph-based…
Anomaly DetectionGraph Anomaly DetectionGraph LearningNode ClassificationSeismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2D or 3D, incorporates global features, i…
Graph LearningIntroducing Graph Learning over Polytopic Uncertain Graph
This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a k…
Graph LearningCharacterizing the Influence of Topology on Graph Learning Tasks
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the fundamental problem of understanding and…
Graph LearningStochastic Block ModelPathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
The diagnosis and prognosis of cancer are typically based on multi-modal clinical data, including histology images and genomic data, due to the complex pathogenesis and high heterogeneity. Despite the advancements in dig…
Graph LearningGraph Neural NetworkPrognosisRepresentation Learning+3Spectral GNN via Two-dimensional (2-D) Graph Convolution
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph learning. As an essential part of spectral GNNs, spectral graph convolution extracts crucial frequency information in graph data, leading to…
Graph LearningModel Selection with Model Zoo via Graph Learning
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be computationally intensive and costly, es…
DiversityGraph LearningmodelModel SelectionOn the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
Graph transformers have recently received significant attention in graph learning, partly due to their ability to capture more global interaction via self-attention. Nevertheless, while higher-order graph neural networks…
Graph AttentionGraph LearningPolynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling …
Graph LearningGLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting the right GL model becomes increasingly…
Graph LearningLink PredictionmodelModel Selection+1CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item representations, which need to capture both the…
cross-modal alignmentGraph LearningMarketingRelation+1Continual Learning for Smart City: A Survey
With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learn…
Continual LearningFederated LearningGraph LearningSurveyDE-HNN: An effective neural model for Circuit Netlist representation
The run-time for optimization tools used in chip design has grown with the complexity of designs to the point where it can take several days to go through one design cycle which has become a bottleneck. Designers want fa…
Graph LearningTG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search
Neural Architecture Search (NAS) is a powerful technique for discovering high-performing CNN architectures, but most existing methods rely on costly training or extensive sampling. Zero-shot NAS offers a training-free al…
Graph LearningNeural Architecture Search