Abstract
Telecom users constitute a huge, but relatively sparse social network. Community discovery has been a research topic of data mining. Traditional algorithms are greatly influenced by outliers. This paper presents a new algorithm based on social triangle theory. Experiments show that the new algorithm is effective.
Highlights
Short messages and call records are an important part of social media network
The division of community structure is closely related to the segmentation of images in computer science and the hierarchical clustering in sociology [2]
This paper utilizes the idea of social triangle theory and proposes a community discovery algorithm based on the improved triangle theory, and designs and conducts some experiments to demonstrate its effectiveness
Summary
Short messages and call records are an important part of social media network. One needs to dig out the various social relations among users or the behavior habit of the users from the call and SMS records, to develop more suitable pricing or business package politics for users, and to provide users with better service to attract more users. To achieve the above objectives, the community partition of telecommunication users is a prospective direction. Before the birth of Internet, people began to study the structure of networked community, such as early biomedical research on the protein structure [1]. The division of community structure is closely related to the segmentation of images in computer science and the hierarchical clustering in sociology [2]. This paper utilizes the idea of social triangle theory and proposes a community discovery algorithm based on the improved triangle theory, and designs and conducts some experiments to demonstrate its effectiveness
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