RSkNN: kNN Search on Road Networks by Incorporating Social Influence

Abstract

Although $k$ NN search on a road network $G_r$ , i.e., finding $k$ nearest objects to a query user $q$ on $G_r$ , has been extensively studied, existing works neglected the fact that the $q$ 's social information can play an important role in this $k$ 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: $k$ 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.