Social Relationship Modeling and Community Detection Based on Trajectory Data

With the maturity of portable devices and positioning technology, the amount of trajectory data is increasing. The hidden social relationship of mobile users in trajectory data can help to provide more accurate location-based services. In this paper, a method of building social relationship model based on trajectory data is proposed. By distinguishing the meeting situation of mobile users, the meeting times and the duration of contacts are calculated, based on which a social relationship matrix is obtained. On this basis, the existing complex network community detection algorithms are applied for community partitioning based on social relationships, the community size, modularity and time complexity are analyzed and compared, which provides a guidance for choosing different community detection algorithm for trajectory data in different application scenarios.

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