WCSE 2017
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.0108

Scalable Key Node Set Mining Method in Social Network Community Based on Topological Potential and Uncertainty Measure

Hongbo Li, Jinbo Bai, Jianpei Zhang, Jing Yan, Jianping Chen

Abstract— For cost savings or on-demand product promotion, e-commerce enterprises always pay great attention to key nodes in social networks. However, the exiting key nodes mining methods or indexes in networks have some deficiencies, such as not being capable of evaluating or mining communities’ key nodes in networks and mining nodes playing roles of bridge between communities. Aiming at these drawbacks, based on topological potential and uncertainty measure, we propose a scalable key node set mining method in social network community. The method firstly puts the nodes in a network into two categories, the inner nodes and the boundary nodes, secondly ranks the inner nodes by their topological potential and ranks the boundary nodes by their identity uncertainty measure, thirdly searches the two ranking list respectively with parameters provided by the e-commerce enterprises, and then gets the key node set. The experiments show that the method is plausibility and validity.

Index Terms— key node set, social network, topological potential, uncertainty measure.

Hongbo Li, Jianping Chen
School of Computer Science, School of Software, Zhaoqing University, CHINA
Jinbo Bai
Economics & Management College, Zhaoqing University, CHINA
Jianpei Zhang, Jing Yan
College of Computer Science and Technology, Harbin Engineering University, CHINA


Cite: Hongbo Li, Jinbo Bai, Jianpei Zhang, Jing Yan, Jianping Chen, "Hongbo Li, Jinbo Bai, Jianpei Zhang, Jing Yan, Jianping Chen," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 621-627, Beijing, 25-27 June, 2017.