Abstract—Virtual community (VC) arises rapidly and influences many aspects of human life styles in real world. Differentiated from traditional way to advertise products/services, VC also enables consumers to participate in interaction activities related to products via threads, learn greater insight about products in deep level while improve consumer loyalty. Most of the extant research did not emphasize or lack of effective methods on how to gain deep learning of product and explain the uniformity of users’ importance in VC. In this paper, based on knowledge in complex network, generalised variance of degree in directed network is proposed to ascertain uniformity of directed network, which is an innovative methodology. Research conclusions can guide enterprises more in-depth understanding of the complex network theory and its application to social network analysis (SNA) with big data streams.

Index Terms—Virtual community, complex network, generalised variance of degree.

Department of Statistics, Chinese University of Hong Kong, Hong Kong, China. School of Management, Shandong University, Jinan, Shandong 250100, China. School of Mathematics, Shandong University, Jinan, Shandong 250100, China. Faculty of Information and Technology, Monash University, Melbourne 3800, Australia (e-mail: rongxiaoxia@sdu.edu.cn, vincent.cs.lee@monash.edu).

Cite: Zhen Zhang, Qingchun Meng, Xiaoxia Rong, Vincent. C. S. Lee, " Application of Novel Features in Complex Network for Analyzing Virtual Community," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 10, no. 4, pp. 312-320, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).