Results 1 to 10 of about 184,942 (303)
Metron, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li-Chun Zhang, Martina Patone
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Evaluating graph neural networks under graph sampling scenarios [PDF]
PeerJ Computer Science, 2022 Background It is often the case that only a portion of the underlying network structure is observed in real-world settings. However, as most network analysis methods are built on a complete network structure, the natural questions to ask are: (a) how ...
Qiang Wei, Guangmin Hu
doaj +3 more sources
Graph Sampling Through Graph Decomposition and Reconstruction Based on Kronecker Graphs [PDF]
Journal of Systemics, Cybernetics and Informatics, 2022 The connectedness of the social network gives rise to a new challenge of how to efficiently sample the network and keep the graph properties and topology properties as well.
Shen Lu, Les Piegl, Richard S. Segall
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Hyperspectral target detection based on graph sampling and aggregation network. [PDF]
PLoS ONETo comprehensively utilize the spectral information encapsulated within hyperspectral images and more effectively handle the intricate and irregular structures among pixels in complex hyperspectral data, a novel graph sampling aggregation network model ...
Tie Li, Hongfeng Jin, Zhiqiu Li
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Greedy Sampling of Graph Signals [PDF]
IEEE Transactions on Signal Processing, 2018 14 pages, 14 figures.
Luiz F O Chamon, Alejandro Ribeiro
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Federated Knowledge Graph Query Based on Graph Structure Feature Sampling Data Summary [PDF]
Jisuanji gongcheng, 2023 The federated system processes SPARQL queries by constructing an effective query plan to guide query execution.The data summary index file captures the structure and semantic information of Resource Description Framework(RDF) datasets, essential for the ...
GAO Feng, LI Qiu, GU Jinguang
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SAMPLING THEORY FOR GRAPH SIGNALS ON PRODUCT GRAPHS [PDF]
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018 Accepted to GlobalSIP ...
Rohan Varma, Jelena Kovacevic
openaire +2 more sources
Efficient Non-Sampling Graph Neural Networks
Information, 2023 A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operation ...
Jianchao Ji +5 more
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Multi-Channel Sampling on Graphs and Its Relationship to Graph Filter Banks
IEEE Open Journal of Signal Processing, 2023 In this article, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited ones in many applications, such as piecewise constant/smooth graph signals and union of bandlimited graph ...
Junya Hara, Yuichi Tanaka
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Large Graph Sampling Algorithm for Frequent Subgraph Mining
IEEE Access, 2021 Large graph networks frequently appear in the latest applications. Their graph structures are very large, and the interaction among the vertices makes it difficult to split the structures into separate multiple structures, thus increasing the difficulty ...
Tianyu Zheng, Li Wang
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