Research on Sampling Estimation Method for Complex Networks-Oriented

Abstract

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.