RSkNN kNN Search on Road Networks by Incorporating Social Influence - 2016


Although k NN search on a road network G_r , i.e., finding k nearest objects to a question user q on G_r , has been extensively studied, existing works neglected the fact that the q 's social information will play an important role during this k NN query. Many real-world applications, like location-based social networking services, need such a query. During this paper, we have a tendency to study a brand new drawback: k NN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence. One essential challenge of the problem is to hurry up the computation of the social influence over giant road and social networks. To address this challenge, we propose 3 efficient index-primarily based search algorithms, i.e., road network-based mostly (RN-based), social network-based (SN-primarily based), and hybrid indexing algorithms. In the RN-based mostly algorithm, we use a filtering-and-verification framework for tackling the exhausting problem of computing social influence. In the SN-based algorithm, we have a tendency to embed social cuts into the index, so that we tend to speed up the question. In the hybrid algorithm, we propose an index, summarizing the road and social networks, primarily based on that we will acquire query answers efficiently. Finally, we tend to use real road and social network knowledge to empirically verify the efficiency and efficacy of our solutions.

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