论文标题
大型道路网络上的Top-K社区相似性搜索（技术报告）
Top-k Community Similarity Search Over Large-Scale Road Networks (Technical Report)
论文作者
论文摘要
随着基础设施的城市化和发展，在城市/城市规划，当地社区的社会研究以及房地产机构的社区建议等许多实际应用中，社区搜索对道路网络的搜索变得越来越重要。在本文中，我们提出了一个新的问题，即在道路网络上，即Top-K社区相似性搜索（Top-KCS2），该搜索有效，有效地获得了与道路网络图中给定的查询社区最相似的K空间社区。为了在本文中有效，有效地解决Top-KCS2问题，我们将在空间社区之间设计一个有效的相似性度量，并提出一个框架来检索Top-KCS2查询答案，该框架集成了离线预处理和在线计算阶段。此外，我们还考虑了一个变体的，即连续的Top-K社区相似性搜索（CTOP-KCS2），该搜索社区沿查询线段不断移动。我们开发了一种有效的算法将查询线段分为间隔，为每个间隔逐渐获得类似的候选社区，并定义实际的CTOP-KCS2查询答案。已经对真实和合成数据集进行了广泛的实验，以确认我们提出的TOP-KCS2和CTOP-KCS2在各种参数设置下的效率和有效性
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
