Title: Dynamic governance of social network based on dynamic optimisation algorithm: a new perspective of AI system

Authors: Fengzhang Chen; Fengwen Chen; Fangnan Liao; Lu Zhang; Jing Zhang

Addresses: College of Computer Science, Chongqing University, 174, Shazheng St., Shapingba Dist., Chongqing, China ' School of Economics and Business Administration, Chongqing University, 174, Shazheng St, Shapingba Dist., Chongqing, China ' College of Economics and Management, Southwest University, No. 2 Tiansheng Road, Beibei Dist., Chongqing, China ' School of Economics and Business Administration, Chongqing University, 174, Shazheng St., Shapingba Dist., Chongqing, China ' School of Economics and Business Administration, Chongqing University, 174, Shazheng St, Shapingba Dist., Chongqing, China

Abstract: This study optimises the dynamic evolution of an entrepreneurial social network. Exploiting a dynamic optimisation algorithm-based (DOA-based) AI system as the exploratory model and unique customer-purchase-level big data based on Hadoop, we can maximise start-ups' revenue while minimising the dynamic governing cost of a social network to select the right time to evolve the entrepreneurial social network in the context of uncertainty in customer behaviour and the response of the social network. We find that the dynamic optimisation algorithm can effectively improve the dynamic governance of social networks through empirical testing of a real start-up's social network evolution problem. Moreover, the improvement is affected by the exploration efficiency, evolution efficiency, unit resource reliance cost, revenue and profit. These findings are great importance for exploring the roles played by artificial intelligence in entrepreneurial social networks, including measurement, dynamic research and governance.

Keywords: dynamic optimisation; social network; network dynamism; dynamic network governance; AI system.

DOI: 10.1504/IJTM.2020.112102

International Journal of Technology Management, 2020 Vol.84 No.1/2, pp.25 - 49

Received: 07 May 2019
Accepted: 20 Apr 2020

Published online: 18 Dec 2020 *

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