Community detection from location-tagged networks
Many real world systems or web services can be represented as a network such as social networks and transportation networks. In the past decade, many algorithms have been developed to detect the communities in a network. However, the impact of locations on community has not been fully investigated by the research literature. In this paper, we propose a method to determine if a location-based community detection method is suitable for a given network and provide a new community detection algorithm that pushes the location information into the community detection. We test our proposed method on both synthetic data and real world network datasets. The results show that the communities detected by our method distribute in a smaller area compared with the traditional methods and have the similar or higher tightness on network connections.