Narrow Results

Fluctuations in interconnectedness within different industrial sectors are hence critical not only for financial market stability but also for the development of real economy. Thus, this paper contractures the varying network structure and estimates the tail risk spillover linkages at the industry level by applying the TENET (Tail-Event driven NETwork) framework on China’s 24 sectors from 2007 to 2018. There is a higher level of risk spillovers and spatial linkages between sectors during turmoil periods, and the centrality indicators can effectively identify the systemically important industries. Notably, it is more comprehensive and accurate to judge the systemic importance node by considering its location and connection in the network rather than just its internal attributes. Furthermore, the high-order polynomial regressions show that degree and eigenvector centrality have a significantly positive effect on risk contagion, while closeness centrality has a two-regime (“both robust and fragile”) contagion mechanism. From a policy point of view, regulators could formulate early-warning measures based on the impact mechanism and spread paths of systemic risk, especially in financial turmoil periods. Meanwhile, they should pay more attention to preventing the aggregate risks of the most influential sectors.

It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory–inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.

Innovation is crucial to a company’s competitive advantage and employees play an important role in generating innovation within a company. Based on social capital theory, we proposed a new type of social network: the employee mobility network, and explored the impact of employee mobility on innovation. Specifically, we examined the role of both employee turnover rate, and an organisation’s centrality in the employee mobility network in predicting innovation. We collected data from World Intellectual Property Organisation (WIPO), Talentale, and Forbes Global 2000 to test our hypotheses. The results showed that turnover rate had a significantly inverted-U curve relationship with innovation, and both degree and closeness centralities of an organisation in the employee mobility network had a significant positive relationship with innovation. Based on the results, we suggest that companies should find a balanced value for their turnover rate to get the highest return in innovation. Also, we suggest that companies should improve social influence in employee mobility networks in order to attract talent and increase company innovation.

This study examines the benefits of network centrality (NC) in creating technological diversity. Given the role of NC in access to external technological knowledge (TK), this study uses network density and R&D resources as moderators to explore the consequences of using TK for technological diversity. The results show that NC alone does not have a positive effect on technological diversity. However, network density and R&D resources, respectively, moderate the positive relationship between NC and technological diversity. Additionally, when influenced by these two moderators simultaneously, the creation of technological diversity heavily depends on the extent to which centrally positioned firms invest in R&D resources. In this study, R&D resources play a stronger complementary role in NC than network density does.

Transportation is the most basic element in the development of tourism, with expressways being the main channel for tourism transportation in the era of driving tours. The relationship between transportation and tourism development has always been one of the hot topics in tourism geography study. Since the expressway service area is the main node of tourist service facilities in the expressway transportation network, we conducted a field research on 125 service areas and 177 tourist attractions in Guizhou Province, and obtained relevant data. By using the centrality measures for social network analysis, and combining the kernel density analysis with bivariate spatial autocorrelation analysis, this paper explores the centrality characteristics of expressway transportation network, the spatial distribution regularity of tourist attractions, and the spatial coupling between them. The main study conclusions are as follows: (1) The expressway service areas in Guizhou Province gradually decrease from a single core to the periphery. (2) The spatial distribution of tourist attractions presents a weak circular structure with multiple cores in a scattered manner, and the regional differences are apparent. (3) There is positive spatial correlation between the expressway networks and tourist attractions. The spatial coupling between the expressway transportation network and tourist attractions can be divided into four types of clusters: high–high cluster, high–low cluster, low–high cluster, and low–low cluster, with high–high and low–low clusters as the main types. The high–high cluster is the area with the best coupling effect and the most mature tourism. The high–low and low–high clusters have a mismatch between transportation and tourism resources. For such clusters, the spatial coupling can be realized by changing the functions of the service area or improving the quality of the tourist attraction. They have great development potential. The low–low cluster is far away from tourist attractions and markets, thus it has a small value of tourism development, and it is the critical area for future improvement. The conclusion of this study can provide a theoretical basis and scientific suggestions for promoting integrated and coordinated sustainable development of transportation and tourism.