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With the urbanization and development of infrastructure, the community searchover road networks has become increasingly important in many real applicationssuch as urban/city planning, social study on local communities, and communityrecommendations by real estate agencies. In this paper, we propose a novelproblem, namely top-k community similarity search (Top-kCS2) over roadnetworks, which efficiently and effectively obtains k spatial communities thatare the most similar to a given query community in road-network graphs. Inorder to efficiently and effectively tackle the Top-kCS2 problem, in thispaper, we will design an effective similarity measure between spatialcommunities, 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 communitysimilarity search (CTop-kCS2), where the query community continuously movesalong a query line segment. We develop an efficient algorithm to split queryline segments into intervals, incrementally obtain similar candidatecommunities for each interval and define actual CTop-kCS2 query answers.Extensive experiments have been conducted on real and synthetic data sets toconfirm the efficiency and effectiveness of our proposed Top-kCS2 and CTop-kCS2approaches under various parameter setting

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