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With data from epitaphs of elites in ancient China, we construct the elite social networks of the Tang-dynasty and the Song-dynasty. The complexity theory is then introduced to unveil associations between the elite social networks and the history. The top influential elites in ancient China are identified from the elite social networks. We detect the community structure formed with strongly connected elites, and investigate the interactions between the communities. It is observed that the elite social networks of the Tang-dynasty and the Song-dynasty exhibit the neutral and disassortative mixing patterns, respectively. Additionally, this work quantitatively supports the Tang-Song Transition Hypothesis. • Reconstruct the elite social networks (ESNs) of ancient China. • Identify the top influential elites in ancient China. • Unveil the community structure of the ESNs. • Present the neutral and disassortative patterns in the ESNs. [ABSTRACT FROM AUTHOR]

In this paper, we analyze information flows between communities of financial markets, represented as complex networks. Each community, typically corresponding to a business sector, represents a significant part of the financial market and the detection of interactions between communities is crucial in the analysis of risk spreading in the financial markets. We show that the transfer entropy provides a coherent description of information flows in and between communities, also capturing non-linear interactions. Particularly, we focus on information transfer of rare events—typically large drops which can spread in the network. These events can be analyzed by Rényi transfer entropy, which enables to accentuate particular types of events. We analyze transfer entropies between communities of the five largest financial markets and compare the information flows with the correlation network of each market. From the transfer entropy picture, we can also identify the non-linear interactions, which are typical in the case of extreme events. The strongest flows can be typically observed between specific types of business sectors—financial sectors is the most significant example. [ABSTRACT FROM AUTHOR]

This paper introduces policy incentive factors to explore their impact on innovation diffusion in enterprise clusters, and reveals the relationship between policy incentives and innovation adoption ratio through dynamic correlation simulation of policy incentive intensity and the type and scale of enterprise clusters. The research results show that policy incentives have short-term timeliness, and the impact of incentives over time will submerge the impact of interconnected enterprises, and its effect is related to the incentive evaluation value of related node enterprises. In the initial enterprise innovation is more, policy incentives can further enhance the overall innovation adoption ratio, while in the initial enterprise innovation is less, it can only decelerate recession. Besides, the effect of policy incentive is closely related to the type of enterprise cluster. The key factor of that is the choice of incentive intensity should match the type and scale of enterprise cluster. Moreover, the incentive intensity of traditional enterprise cluster is positively correlated with the scale of cluster, while the incentive intensity of innovative enterprise cluster is negatively correlated with the scale of cluster. In addition, the local incentive effect is better than that of overall incentives. Targeted incentives for limited target groups are more conducive to innovation diffusion. [ABSTRACT FROM AUTHOR]

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies. [ABSTRACT FROM AUTHOR]