Titlebook: Complex Networks XIV; Proceedings of the 1 Andreia Sofia Teixeira,Federico Botta,Giuseppe Man Conference proceedings 2023 The Editor(s) (if

Julienne

书目名称Complex Networks XIV影响因子(影响力)




书目名称Complex Networks XIV影响因子(影响力)学科排名




书目名称Complex Networks XIV网络公开度




书目名称Complex Networks XIV网络公开度学科排名




书目名称Complex Networks XIV被引频次




书目名称Complex Networks XIV被引频次学科排名




书目名称Complex Networks XIV年度引用




书目名称Complex Networks XIV年度引用学科排名




书目名称Complex Networks XIV读者反馈




书目名称Complex Networks XIV读者反馈学科排名

palette

Springer Proceedings in Complexityhttp://image.papertrans.cn/c/image/231502.jpg

inventory

HAWK

https://doi.org/10.1007/0-387-33745-8 studied navigation strategies we obtained theoretical and numerical values for the graph mean first passage times as an indicator for the searching efficiency. The experiments with generated and real networks show that biasing based on inverse degree, persistence and local two-hop paths can lead to

Barter

有杂色

https://doi.org/10.1007/0-387-33745-8iations between transcription factors and target genes, are responsible for representing and controlling this gene expression and regulate the response of an organism to environmental changes. In this paper, we extend the study of these systems by applying different community detection algorithms on

有杂色

photopsia

Isotope Notation and Measurement,o a centrality measure and remove top nodes according to a budget. The goal is to exploit the network features efficiently to dismantle them with a minimal budget. Few works are linked to the network mesoscopic properties in the literature, although it is well-admitted that communities or core-perip

Atrium

云状

Isotopic Properties of Selected Elements,M, a trainable algorithm for network dismantling via node-removal. The approach is based on Geometric Deep Learning and that merges the Graph Dismantling Machine (GDM) [.] framework with the CoreHD [.] algorithm, by attacking the 2-core of the network using a learnable score function in place of the