The way to uncover community structure with core and diversity

Abstract

Communities are ubiquitous in nature and society. Individuals that share common properties often self-organize to form communities. Being able to identify community structure could help us understand and explore complex systems efficiently. Avoiding the shortages of computation complexity, pre-given information and unstable results in different run, in this paper, we propose one simple and efficient method to try to give a deep understanding of the emergence and diversity of communities in complex systems. By introducing rational random selection, our method reveals the hidden deterministic and normal diverse community states of community structure. To demonstrate this method, we test it with real-world systems. The results show that our method could not only detect community structure with high sensitivity and reliability, but also could provide instructional information about our normal diverse community world and the hidden deterministic community world by giving out the core-community, the real-community, the tide (boundary) and the diversity. This is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in complex systems.

Statistics of the efficiency in community detection by most similar node pairs

“…Community detection by most similar node pairs has been proposed as an efficient method for community analysis [27,28]. In this paper, we give a two level community structure analysis for the SSCI journal system with five strategies for the selection of most similar node pairs.…”