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An Improved Influential Cover Set Mining Algorithm

Volume 14, Number 11, November 2018, pp. 2612-2623
DOI: 10.23940/ijpe.18.11.p7.26122623

Jia Liua,b, Wei Chena,b, Ziyang Chena,c, and Huijuan Liua

aSchool of Information Science and Engineering, YanShan University, Qinhuangdao, 066004, China
bDepartment of Information Engineering, Hebei University of Environmental Engineering, Qinhuangdao, 066102, China
cSchool of Information and Management, Shanghai Lixin University of Accounting and Finance, Shanghai, 201620, China

(Submitted on August 20, 2018; Revised on September 22, 2018; Accepted on October 10, 2018)

Abstract:

The influential cover set (ICS) problem is a hot research issue in social networks and road networks. Considering that existing methods cannot take into account both the efficiency and the accuracy of results, we propose a partition-based influential cover set mining algorithm with the index. Firstly, we create the inverted index for each attribute to be queried, thus avoiding traversing all nodes while querying the node covered attributes and reducing the query time with high accuracy. Then, we design the pruning strategy according to the upper bound of cover-group for filtering, which reduces the number of the partition combination in the treatment on each tuple in the linked list of partitions, reduces the overall computation, and improves the processing speed and efficiency. Our experimental results on 15 real datasets verify the efficiency of our method in terms of different metrics, including indexing time, accuracy of results, influence of results, and query processing time.

 

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