RS k NN: k NN Search on Road Networks by Incorporating Social Influence

• Published in: IEEE transactions on knowledge and data engineering Vol. 28; no. 6; p. 1575
• Main Authors: Ye Yuan, Lian, Xiang, Chen, Lei, Sun, Yongjiao, Wang, Guoren
• Format: Journal Article
• Language: English
• Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.06.2016
• Subjects: Algorithms; Social networks
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2016.2518692

Although [Formula Omitted]NN search on a road network [Formula Omitted], i.e., finding [Formula Omitted] nearest objects to a query user [Formula Omitted] on [Formula Omitted], has been extensively studied, existing works neglected the fact that the [Formula Omitted]'s social information can play an important role in this [Formula Omitted]NN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper, we study a new problem: [Formula Omitted]NN search on road networks by incorporating social influence (RS k NN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.