Community Detection from Location-Tagged Networks

TL;DR

This paper introduces a community detection method that incorporates geographic location data, improving the identification of communities in location-tagged networks by considering both spatial proximity and network structure.

Contribution

The paper presents a novel community detection algorithm that integrates location information, addressing the gap in existing methods that overlook geographic influence.

Findings

Communities detected are geographically more compact.

The method achieves similar or higher network tightness.

Effective on both synthetic and real-world data.

Abstract

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 using connections between nodes. However in many real world networks, the locations of nodes have great influence on the community structure. For example, in a social network, more connections are established between geographically proximate users. The impact of locations on community has not been fully investigated by the research literature. In this paper, we propose a community detection method which takes locations of nodes into consideration. The goal is to detect communities with both geographic proximity and network closeness. We analyze the distribution of the distances between connected and unconnected nodes to measure the influence of location on the network structure on two real location-tagged social networks. We propose a method to determine if a location-based community detection method is suitable for a given network. We propose 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.