Recently, there has been growing interest in social network analysis. Graph models for social network analysis are usually assumed to be a deterministic graph with fixed weights for its edges or nodes. As activities of users in online social networks are changed with time, however, this assumption is too restrictive because of uncertainty, unpredictability and the time-varying nature of such real networks. The existing network measures and network sampling algorithms for complex social networks are designed basically for deterministic binary graphs with fixed weights. This results in loss of much of the information about the behavior of the network contained in its time-varying edge weights of network, such that is not an appropriate measure or sample for unveiling the important natural properties of the original network embedded in the varying edge weights. stochastic graphs, in which weights associated with the edges are random variables, can be a suitable model for complex social network. In this paper, according to the principle that Social networks are one of the cases where the distribution of links to nodes is according to the power law that we proposed Levy's initial flight automation sampling algorithm for random graphs, which is a good model for complex social networks. Using Levy Flight instead of gait-based learning that guarantees part of the solution is not separate from the present solution, therefore, it endores an optimizer tolerance, local optimal tolerance, and early convergence. In order to study the performance of the proposed sampling algorithms, several experiments are conducted on real and synthetic stochastic graphs. These algorithms ‘performance is evaluated based on the relative cost, Kendall correlation coefficient, Kolmogorov–Smirnov D statistics, and relative error.

最近，人们对社交网络分析越来越感兴趣。用于社交网络分析的图模型通常被假定为一个确定性图，其边或节点具有固定的权重。然而，由于在线社交网络中用户的活动随时间而变化，由于这种真实网络的不确定性、不可预测性和时变性质，这种假设过于严格。现有的复杂社交网络的网络度量和网络采样算法基本上是为具有固定权重的确定性二元图设计的。这导致丢失了很多关于网络行为的信息，这些信息包含在网络的时变边缘权重中，这不是揭示嵌入在不同边缘权重中的原始网络的重要自然属性的适当度量或样本。随机图，其中与边相关的权重是随机变量，可以是复杂社交网络的合适模型。在本文中，根据社交网络是节点链接分布符合幂律的原则之一，我们提出了 Levy 的随机图初始飞行自动化采样算法，这是复杂社交的一个很好的模型。网络。使用 Levy Flight 而不是基于步态的学习，保证部分解决方案与当前解决方案不分离，因此，它支持优化器容差、局部最优容差和早期收敛。为了研究所提出的采样算法的性能，在真实和合成随机图上进行了一些实验。这些算法的性能是根据相对成本、Kendall 相关系数、Kolmogorov-Smirnov D 统计量和相对误差来评估的。