Narrow Results

Venture capital is an important force in promoting technological innovation and social progress. Research regarding the attention on venture capital can help understand the development and social influence of venture capital, which is conducive to the policy guidance and incentive to the industry. With the rapid development of information technology, the Internet has become the main source and dissemination channel of information, and search engines have become an important interface for information. Through a deep analysis of the search index of venture capital, we can find information that is more important. Based on the principle of time series visualization, we have transformed the time series data of attention on venture capital into complex networks and analyzed its network characteristics. We collected the Baidu index of Chinese venture capital from January 1, 2018 to November 25, 2019 and constructed a time series network based on PC plus mobile search, PC search, and mobile search. The results show that the convenience of the mobile terminal offers makes it the primary mode of tracking the industrial dynamics of venture capital, especially hot news. Relatively, PC terminal search is more stable than mobile search, more focused on industry reports, and refers to long-term followers of venture capital. Both degree distribution and centrality distribution of the three networks show that abnormal peaks and lows are few and the number of key time points in the time series of search on venture capital is insignificant. However, the clustering results show obvious segmentation effect that the peak effect of hot news is evident.

Baidu search engine is the most common one adopted by Chinese Internet users, and Baidu index provides a platform to capture the behaviors of massive users on Baidu, which is one important statistical tool to mine the Internet users’ behaviors and characteristics in China. Here, we utilize the Baidu index data on greenhouse gas from January 1, 2011 to November 29, 2019, to perform the related statistical analyses at first. Then, on the basis of Baidu index time series data, the corresponding network is constructed by use of the visibility graph method. Finally, the topology of the generated network is analyzed from different perspectives. Our results indicate that people’s attention to greenhouse gases obeys the power-law distribution, and we can identify the significant nodes and find some outliers in time series data by use of the topological properties of networks. Taking together, the current model offers a novel means to represent and depict the time series data of Baidu index through the complex network analysis.

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.