A Novel Privacy Preserving Framework for Large Scale Graph Data Publishing
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2019.2931903
Abstract: The need to efficiently store and query large scale graph datasets is evident in the growing number of data-intensive applications, particularly to maximize the mining of intelligence from these data (e.g., to inform decision making).… read more here.
Keywords: privacy preserving; large scale; graph; scale graph ... See more keywords
Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3158280
Abstract: In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive. It frequently induces the trouble of model overfitting and performance degradation for the supervised methodologies in HSI classification (HSIC). Fortunately,… read more here.
Keywords: graph prototypical; network; cross scale; classification ... See more keywords
Optimal Representation of Large-Scale Graph Data Based on Grid Clustering and K2-Tree
Sign Up to like & getrecommendations! Published in 2020 at "Mathematical Problems in Engineering"
DOI: 10.1155/2020/2354875
Abstract: The application of appropriate graph data compression technology to store and manipulate graph data with tens of thousands of nodes and edges is a prerequisite for analyzing large-scale graph data. The traditional K2-tree representation scheme… read more here.
Keywords: large scale; graph data; scale graph; graph ... See more keywords