Top-k Community Similarity Search Over Large Road-Network Graphs

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-kCS
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), which efficiently and effectively obtains spatial communities that are the most similar to a given query community over road-network graphs. In order to efficiently and effectively tackle the Top-kCS
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problem, in this paper, we will design an effective similarity measure between communities, and propose a framework for retrieving Top-kCS
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query answers. Extensive experiments have been conducted on real and synthetic data sets to confirm the efficiency and effectiveness of our proposed Top-kCS
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approach under various parameter settings.