Spatial-Aware Local Community Detection Guided by Dominance Relation

TL;DR

This paper introduces a novel spatial-aware local community detection algorithm guided by dominance relations, which effectively finds communities using only local information and improves efficiency over existing methods.

Contribution

The paper proposes a new local community detection algorithm based on dominance relations that operates with only local information and enhances efficiency with an approximate version.

Findings

The approximate algorithm outperforms comparison algorithms in experiments.

The method effectively detects spatial-aware communities without global network information.

The approach addresses parameter sensitivity and incomplete network challenges.

Abstract

The problem of finding the spatial-aware community for a given node has been defined and investigated in geo-social networks. However, existing studies suffer from two limitations: a) the criteria of defining communities are determined by parameters, which are difficult to set; b) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose an approximate algorithm. Experimental results demonstrate that the proposed approximate algorithm outperforms the comparison algorithms.