The research of social networks is currently popular, as it provides an opportunity for the investigation of disparate theoretical approaches. The analysis of social networks requires mathematical and computational theory, in addition to knowledge from social sciences.

Micro-blogging social networks are the subject of this paper. It uses the Chinese Twitter-like social network, Sina Weibo, as an illustrative example and case study. The question raised is whether there is a better algorithm or solution for the problem of searching for people to follow. The contribution of the paper is an approach for assisting micro-bloggers in dynamically building up social network links. The paper presents an improvement of the PageRank algorithm and describes an implementation of PageRank that fits micro-blogging social networks. The proposed method utilizes the adapted tag extension procedure. The paper outlines a software architecture that supports empirical measurement, exploiting the available data from the Chinese social network. To demonstrate the feasibility of the approach, the authors developed a search system and designed experiments to evaluate the performance of the proposed solution from various viewpoints. Three domains were considered: occupation, academic, and companies.

The implemented search system attempts to provide a good answer to the question of “what types of people are worth following” in a social network environment. For those interested in social network-related research, this paper may provide further ideas to carry out investigations on other social networks, applying novel algorithms and architecture designs.