符号网络预测准确度及时间代价的优化

doi:10.3969/j.issn.1007-7375.e16-4208

工业工程 ›› 2017, Vol. 20 ›› Issue (1): 59-64.doi: 10.3969/j.issn.1007-7375.e16-4208

符号网络预测准确度及时间代价的优化

滕少华, 苏庆佳, 刘冬宁, 张巍

广东工业大学计算机学院, 广东广州 510006

收稿日期:2017-01-01 出版日期:2017-02-28 发布日期:2017-03-13
作者简介:滕少华(1962-),男,江西省人,教授,博士,主要研究方向为协同计算、大数据、数据挖掘、网络安全.
基金资助:
国家自然科学基金资助项目（61402118）；广东省科技计划资助项目（2013B090200017，2013B010401029，2013B010401034，2016B010108007，2015B090901016，201508010067）；广州市科技计划资助项目（201508010067，2013J 4500028，2013J4100004，2016201604030034，201604020145）；广东省教育厅资助项目（粤教高函2015[133]号，粤教高函[2014]97号，ZYGX008）

Research about Optimizing Prediction Accuracy and Time Complexity in Signed Networks

TENG Shaohua, SU Qingjia, LIU Dongning, ZHANG Wei

School of Computer, Guangdong University of Technology, Guangzhou 510006, China

Received:2017-01-01 Online:2017-02-28 Published:2017-03-13

摘要/Abstract

摘要： 符号网络的预测准确度越来越高，但是时间复杂度也越来越难以接受。必须寻找有效预测方法，既保证算法预测准确度高，同时时间复杂度低。本文设计了一个优化算法，使用平衡环算法预测符号，利用函数拟合方法分别拟合预测准确度与步长、时间复杂度与步长的函数关系，分析随步长增加预测准确度与时间复杂度的关系并提出优化方案。实验显示，本文的优化算法能够有效获得预测准确度与时间复杂度的关系。本文可供设计符号预测算法的研究者参考。

关键词: 符号网络, 符号预测, 时间复杂度, 优化

Abstract: In signed networks, different sign predicting algorithms have been proposed. The prediction accuracy of the algorithm is improving, but the time complexity is also increasing. A way must be found to reduce the time complexity. In order to ensure the high prediction accuracy and low time complexity, an optimization algorithm is designed to analyze the relation between prediction accuracy and time complexity with increasing steps and an optimization scheme is also proposed through using the balanced cycle algorithm for predicting sign at first and then fitting the function of prediction accuracy and step, time complexity and step respectively. Experiments show that the optimization algorithm can effectively obtain the relation between prediction accuracy and time complexity. This research can be used in working out design symbol prediction algorithms.

Key words: signed networks, sign prediction, time complexity, optimization

滕少华, 苏庆佳, 刘冬宁, 张巍. 符号网络预测准确度及时间代价的优化[J]. 工业工程, 2017, 20(1): 59-64.

TENG Shaohua, SU Qingjia, LIU Dongning, ZHANG Wei. Research about Optimizing Prediction Accuracy and Time Complexity in Signed Networks[J]. Industrial Engineering Journal , 2017, 20(1): 59-64.

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符号网络预测准确度及时间代价的优化

Research about Optimizing Prediction Accuracy and Time Complexity in Signed Networks

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