Application of Novel Features in Complex Network for Analyzing Virtual Community

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.