• 学术研究 •

### 网络形式背景下的社区划分方法研究

1. 1. 昆明理工大学 数据科学研究中心，昆明 650500
2. 昆明理工大学 理学院，昆明 650500
• 出版日期:2021-08-01 发布日期:2021-08-02

### Research on Community Division Method Under Network Formal Context

LIU Wenxing, FAN Min, LI Jinhai

1. 1. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500, China
2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
• Online:2021-08-01 Published:2021-08-02

Abstract:

Network community division is the basis of concept cognition and pattern learning from social networks, and is also a hot topic in the study of machine learning under the network background. In order to make full use of the advantages of formal concepts and network characteristic values, this paper discusses the problem of network community division based on network formal contexts. It firstly gives the notions of network node centrality and centralization based on the information of network structure and node attributes, which makes the division of network community in a network formal context take into account the characteristics of the network structure and node connotation. Then, the network community concept of network formal context is presented. It not only obtains the formal concept of a traditional formal context, but also includes the network characteristic values of the concept. As a result, the average importance of the concept in the network can be described as well as the different quantity between the average importances. Furthermore, considering the characteristics of multiple roles and network orienta-tion in the division of a social network, the directed network is divided into single-role network and double-role network. Besides, two network community division algorithms are proposed by combining the information of network structure and node attributes, and their time complexities are analyzed. Finally, examples are used to show the effectiveness of the proposed network community division algorithms. The obtained rusults can provide a reference for the further study of network data mining and network concept cognition.