Top-k Community Similarity Search Over Large-Scale Road Networks (Technical Report)

TL;DR

This paper introduces a novel top-k community similarity search problem over large-scale road networks, proposing efficient algorithms and a framework that combines offline preprocessing with online querying, including a variant for moving query communities.

Contribution

It defines the Top-kCS2 problem, designs a new similarity measure, and develops an integrated framework with algorithms for both static and moving community queries.

Findings

The proposed methods are efficient on real and synthetic datasets.

The algorithms effectively identify top-k similar communities.

Experimental results demonstrate high accuracy and scalability.

Abstract

With the urbanization and development of infrastructure, the community search over road networks has become increasingly important in many real applications such as urban/city planning, social study on local communities, and community recommendations by real estate agencies. In this paper, we propose a novel problem, namely top-k community similarity search (Top-kCS2) over road networks, which efficiently and effectively obtains k spatial communities that are the most similar to a given query community in road-network graphs. In order to efficiently and effectively tackle the Top-kCS2 problem, in this paper, we will design an effective similarity measure between spatial communities, and propose a framework for retrieving Top-kCS2 query answers, which integrates offline pre-processing and online computation phases. Moreover, we also consider a variant, namely continuous top-k community similarity search (CTop-kCS2), where the query community continuously moves along a query line segment. We develop an efficient algorithm to split query line segments into intervals, incrementally obtain similar candidate communities for each interval and define actual CTop-kCS2 query answers. Extensive experiments have been conducted on real and synthetic data sets to confirm the efficiency and effectiveness of our proposed Top-kCS2 and CTop-kCS2 approaches under various parameter setting