摘要
针对符号预测算法在预测准确率和算法复杂度方面难以均衡的问题,有效地融合社会学发展规律与网络局部特征,提出一种基于结构平衡理论与地位理论计算节点相似度的符号预测算法。为更好的结合上述两种理论对两节点相似度得分的贡献,引用调节因子,将基于两种理论的相似度得分按照调节因子的权重求和,相似度的得分的正负即为边符号预测的结果。最后将算法在多个不同数据集进行实验,与经典的CN算法和PSNBS算法在预测准确率与算法复杂度两个方面进行对比分析。结果显示该算法在预测准确率方面与经典算法非常接近,但在时间复杂度方面本文比经典算法低一个数量级,明显优于经典算法。
Aiming at the difficulty of balancing the accuracy and complexity of the sign prediction algorithm,this paper effectively integrates the law of social development and the local characteristics of the network,and proposes a sign prediction algorithm based on structural balance theory and status theory to calculate the similarity of nodes.In order to better combine the contribution of the above two theories to the similarity score of the two nodes,this paper uses the regulator to sum the similarity score based on the two theories according to the weight of the regulator,and the positive or negative of the similarity score is the result predicted by the edge symbol.Finally,the algorithm is tested on several different data sets and compared with the classical CN algorithm and PSNBS algorithm in two aspects of prediction accuracy and algorithm complexity.The proposed algorithm is very close to the classical algorithm in terms of prediction accuracy,but in terms of time complexity,it is an order of magnitude lower than the classical algorithm.It is obviously better than classical algorithm.
作者
崔晓丽
薛乐洋
张鹏
CUI Xiaoli;XUE Leyang;ZHANG Peng(School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China;International Academic Center of Complex Systems,Beijing Normal University,Zhuhai 519087,China)
出处
《复杂系统与复杂性科学》
CAS
CSCD
北大核心
2023年第3期68-73,81,共7页
Complex Systems and Complexity Science
关键词
结构平衡理论
地位理论
相似度
符号网络
符号预测
structural balance theory
status theory
similarity
signed network
sign prediction
