# Scalable Community Detection over Geo-Social Network

## TL;DR

This paper introduces a scalable framework for community detection in geo-social networks that combines structural and spatial constraints, offering customizable social measures and efficient algorithms for large datasets.

## Contribution

It decouples spatial and structural constraints in community detection, proposing a flexible framework and a near-linear time approximation algorithm with proven performance.

## Key findings

- The framework effectively detects communities satisfying both constraints.
- The approximation algorithm scales well to large datasets.
- Experimental results confirm the efficiency and accuracy of the methods.

## Abstract

We consider a community finding problem called Co-located Community Detection (CCD) over geo-social networks, which retrieves communities that satisfy both high structural tightness and spatial closeness constraints. To provide a solution that benefits from existing studies on community detection, we decouple the spatial constraint from graph structural constraint and propose a uniform CCD framework which gives users the freedom to choose customized measurements for social cohesiveness (e.g., $k$-core or $k$-truss). For the spatial closeness constraint, we apply the bounded radius spatial constraint and develop an exact algorithm together with effective pruning rules. To further improve the efficiency and make our framework scale to a very large scale of data, we propose a near-linear time approximation algorithm with a constant approximation ratio ($\sqrt{2}$). We conduct extensive experiments on both synthetic and real-world datasets to demonstrate the efficiency and effectiveness of our algorithms.