# Spatial-Aware Local Community Detection Guided by Dominance Relation

arXiv: https://arxiv.org/abs/2203.15215 · HTML: https://mlsanity.com/paper/2203.15215/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Community Detection for Heterogeneous Multiple Social Networks](https://mlsanity.com/paper/2405.04371/) (2024)
- [Pre-trained Prompt-driven Semi-supervised Local Community Detection](https://mlsanity.com/paper/2505.12304/) (2025)
- [Community Search in Time-dependent Road-social Attributed Networks](https://mlsanity.com/paper/2505.12309/) (2025)

## Abstract (arXiv)

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.