Using homogenous partition of coarse graining process, the time series of Hang Seng Index (HSI) in Hong Kong stock market is transformed into discrete symbolic sequences S={S1S2S3…}, Si∈(R, r, d, D). Weighted networks of stock market are con- structed by vertices that are 16 2-symbol strings (i.e. 16 patterns of HSI variations), and encode stock market relevant information about interconnections and interactions between fluctuation patterns of HSI in networks topology. By means of the measure- ments of betweenness centrality (BC) in networks, we have at least obtained 3 highest betweenness centrality uniform vertices in 2 order of magnitude of time subinterval scale, i.e. 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties cannot be found in random networks; here vertices almost have iden- tical betweenness centrality. By comparison to ran- dom networks, we conclude that Hong Kong stock market, rather than a random system, is statistically stable.
We study the evolutionary snowdrift game in a heterogeneous Newman-Watts small-world network. The heterogeneity of the network is controlled by the number of hubs. It is found that the moderate heterogeneity of the network can promote the cooperation best. Besides, we study how the hubs affect the evolution of cooperative behaviours of the heterogeneous Newman-Watts small-world network. Simulation results show that both the initial states of hubs and the connections between hubs can play an important role. Our work gives a further insight into the effect of hubs on the heterogeneous networks.
