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# CS代写|计算机网络代写Computer Networking代考|CSEE4119 Performance of DHT Approximation

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## CS代写|计算机网络代写Computer Networking代考|Performance of DHT Approximation

We investigate the convergence and running time of the two DHT approximation algorithms? Iterative-alg and Sampling-alg. Iterative-alg has one parameter (number of iterations $t$ ) and Sampling-alg has two parameters (maximum number of steps $s$ and number of random walks $c$ ). For Iterative-alg, we investigate its converging speed with respect to $t$. For Sampling-alg, we find when $c>600$, increasing $c$ hardly improves the obtained bounds. Thus, we set $c=600$ and investigate the converging speed of Sampling-alg with respect to $s$. ${ }^{* * }$ Intuitively, since we adopt an exponentially damping factor in the definition of DHT, the converging speed should be fast.** The results are shown inFigure $2.6$ with various $m$ values (the number of nodes that have the same event). For each $m$ value, we randomly select a node $v$ and a set $B$ of $m-1$ nodes and apply the two algorithms to estimate $\tilde{h}(v, B)$. This process is repeated 50 times and the averaged results are reported. As shown in Figure 2.6, both algorithms converge quickly after about five iterations. Note that Iterative-alg gives lower and upper bounds for $\tilde{h}$, while Sampling-alg gives bounds for an estimate of $\tilde{h}$, that is, $\overline{\tilde{h}}$. Comparing Figure 2.6a and b, one can find that the two algorithms converge to roughly the same values. It means empirically Sampling-alg provides a good estimation of $\tilde{h}$.

## CS代写|计算机网络代写Computer Networking代考|Effectiveness on Synthetic Events

To evaluate the effectiveness of our measure, we generate synthetic events on the DBLP graph using the cascade model for influence spread (Kempe, Kleinberg, and Tardos, 2003): At first, a random set of 100 nodes is chosen as the initial $V_q$; then in each iteration nodes joining $V_q$ in the last iteration can activate each currently inactive neighbor with probability $p_{a c}$; we stop when $\left|V_q\right|>10000$. pac can be regarded as representing the level of participation in an event. Intuitively, higher $p_{a c}$ would lead to higher correlation. For all the following experiments, we report the significance estimates as the measure of SSC, that is, $\tilde{\rho}$ in Eq. ((2.8)). $\tilde{\rho}$ can be regarded as approximate $z$ scores. Higher scores mean higher (more significant) correlations, while a score close to 0 indicates that there is no correlation.
The results are shown in Figure 2.8. “Random” means we expand the initial 100 random nodes with randomly selected nodes from the remaining nodes in order to match the corresponding event sizes of cascade model. We can see as $p_{a c}$ increases, the curve of cascade model goes up, while that of “Random” remains around 0.

We further test the performance of gScore by adding noises to the earlier cascade model. $p_{a c}$ is set to $0.2$. Specifically, we break the correlation structure by relocating each black node to a random node in the remaining graph with probability $p_n$ (noise level). $p_n=1$ means all black nodes are randomly redistributed. We report results for different event sizes $(m)$, that is, spread levels.

## CS代写|计算机网络代写计算机网络代考|对合成事件的有效性

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## Matlab代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。