Figure_2A

Dryad2017-01-01 更新2026-04-13 收录

网络干扰传播

加权网络分析

数据链接：

http://datadryad.org/resource/doi:10.5061/dryad.86337/5 数据链接链接失效反馈

官方服务：

资源简介：

Weighted adjacency matrix following application of the disruption propagation model starting from node Re.

创建时间：

2017-01-01

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

图计算加速

加权网络分析

We develop an efficient parallel GPU-based approach to boost the calculation of betweenness centrality on very large and weighted networks. Comprehensive and systematic evaluations on both synthetic

NIAID Data Ecosystem40

Average degree of the weighted networks learned by different approaches.

加权网络分析

生物医学网络

The third row denotes the relative difference between the AD and NC groups.Average degree of the weighted networks learned by different approaches.

Figshare2015-12-03 更新30

Method combinations for generating weighted sample networks.

加权网络分析

社区发现算法

The networks were used as input for fast greedy modularity optimization and Louvain community detection.

Figshare2023-01-06 更新10

Summary of weighted network experiments.

加权网络分析

复杂网络动态建模

Note: w(ej) refers to the weight of edge ej, refers to the strength of node ) and randi{1..9} refers to a randomly selected integers between 1 and 9 (inclusive).Summary of weighted network experiments

Figshare2015-12-02 更新40

Summary of weighted network experiments.

加权网络分析

复杂网络研究

Note: w(ej) refers to the weight of edge ej, refers to the strength of node ) and randi{1..9} refers to a randomly selected integers between 1 and 9 (inclusive). Summary of weighted network experimen

NIAID Data Ecosystem30

Figure_2A

资源简介：

相关数据集