面向复杂网络的抽样估计方法研究

Shih-Chiang Wu
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摘要

大数据作为新一轮产业革命的重要创新要素,在数字经济发展中发挥着重要作用。作为网络数字平台经济的重要载体,研究人员发现,现实网络中的大多数既不是传统的规则网络,也不是完全随机的网络,而是具有一定统计规律的复杂网络。复杂网络具有小世界和无标度的特点。其网络结构复杂,规模庞大,个体相互独立又相互联系。同时,网络中有大量的用户,承载着数以万计的信息。传统的网络分析方法不全面,难以把握网络环境的全貌。因此,本文提出了一种通过改进采样估计来解决网络数据困境的方法。将网络中发现的与研究变量密切相关的数据信息作为辅助信息引入到模型辅助估计方法中,通过网络的局部信息对整体信息进行研究。面对庞大的网络数据规模,它是一项效率高、成本低的重要技术,为快速获取数据和分析结果提供了一种途径。
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Research on Sampling Estimation Method for Complex Networks-Oriented
As an important innovation element in the new round of industrial revolution, big data plays an important role in the development of digital economy. As an important carrier of network digital platform economy, researchers have found that most of the real networks are neither traditional regular networks nor completely random networks, but complex networks with certain statistical rules. Complex network has the characteristics of small world and scale-free. Its network structure is complex, its scale is huge, and its individuals are independent and connected. At the same time, there are a large number of users in the network, carrying tens of thousands of information. The traditional network analysis method is not comprehensive, so it is difficult to grasp the whole picture of the network environment. Therefore, this paper introduces a method to solve the network data dilemma by improving the sampling estimation. The data information closely related to the research variables found in the network is introduced into the model-aided estimation method as auxiliary information, and the whole information is studied through the local information of the network. Facing the huge scale of network data, it is an important technology with high efficiency and low cost, which provides a way to quickly obtain data and analysis results.
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