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Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two \u201cyear-round\u201d complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed \u201cmajor players,\u201d \u201cgatekeeper,\u201d and \u201cvulnerable players,\u201d were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.<\/jats:p>","DOI":"10.1155\/2020\/7195494","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T23:37:21Z","timestamp":1594856241000},"page":"1-19","source":"Crossref","is-referenced-by-count":8,"title":["Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3922-2578","authenticated-orcid":true,"given":"Xiaole","family":"Wan","sequence":"first","affiliation":[{"name":"Management College, Ocean University of China, Qingdao 266100, China"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong, Hong Kong, 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