Simulation Modeling And Empirical Research Of Social Networks For Financial Industry Clusters

The social network of a financial industry cluster is a carrier of sharing capital,information and other resources. Its topology directly influences the efficiency ofresources distribution in the financial industry cluster, and plays a decisive role inavoiding systematic risk. Numerous and varied social relations between financialenterprise make the social network of financial industry clusters presenting dynamicand complex feature. Therefore, modeling and studying its topology characteristics isessential.This article introduced complex network theory and social network analysismethod to study financial industry clusters, and set social networks of financialindustry clusters as the research object. First, it systematically summed to analyzemain parts in the cluster such as financial enterprise and social relations, andsummarized the overall characteristics of social networks. Then, basing on analyzingthe existing modeling results of complex social networks, and aiming at differentimpact vary enterprises in the cluster have on the social network structure, this paperbuilt a social network simulation model based on individual merit. Finally, with thereal data of Shanghai financial industry cluster, the paper comparatively analyzed thesmall world property, nodes between ness, and the robust performance of the realnetwork, simulation network, and the ws network, and put forward some practicalimplications and recommendations.The results showed that the simulation model this article constructed based onindividual merit can be used to restore the topological structure characteristics of thereal social network of financial industry clusters. Through empirical simulation, thearticle found that the social network of real financial industry clusters presentssmall-world characteristics, which is beneficial to improve the efficiency of resourcetransmission, and that nodes with high right are more likely to occupy positions withhigh betweenness. It also concluded that strengthening the risk prevention of nodeswith high right or betweenness, and increasing the amount of redundant connectionhas a positive effect to enhance the network robustness. These findings have somepractical significance for the financial industry cluster development and riskmanagement.