  Financial networks have become extremely useful in characterizing the
structure of complex financial systems. Meanwhile, the time evolution property
of the stock markets can be described by temporal networks. We utilize the
temporal network framework to characterize the time-evolving correlation-based
networks of stock markets. The market instability can be detected by the
evolution of the topology structure of the financial networks. We employ the
temporal centrality as a portfolio selection tool. Those portfolios, which are
composed of peripheral stocks with low temporal centrality scores, have
consistently better performance under different portfolio optimization schemes,
suggesting that the temporal centrality measure can be used as new portfolio
optimization and risk management tools. Our results reveal the importance of
the temporal attributes of the stock markets, which should be taken serious
consideration in real life applications.
