CAJ | 학술논문

针对当前主流的基于网络拓扑结构的链路预测算法普遍存在召回率较低的问题，研究发现一些算法输出的结果中部分正确结果具有互补性，据此采用基于Boosting的集成学习方法对其进行改进。按照网络中节点之间是否存在链接关系，将链路预测问题定义为二分类问题，进一步遵循算法互补的原则选择若干具有代表性的链路预测算法作为弱分类器，基于AdaBoost算法提出并实现了一个新型链路预测算法。在arXiv论文合作网络和电子邮件网络等真实数据集上的实验结果表明，该算法的准确率以及召回率表现均显著优于当前的主流算法。
침대당전주류적기우망락탁복결구적련로예측산법보편존재소회솔교저적문제，연구발현일사산법수출적결과중부분정학결과구유호보성，거차채용기우Boosting적집성학습방법대기진행개진。안조망락중절점지간시부존재련접관계，장련로예측문제정의위이분류문제，진일보준순산법호보적원칙선택약간구유대표성적련로예측산법작위약분류기，기우AdaBoost산법제출병실현료일개신형련로예측산법。재arXiv논문합작망락화전자유건망락등진실수거집상적실험결과표명，해산법적준학솔이급소회솔표현균현저우우당전적주류산법。
The mainstream of current link prediction algorithm based on network topology structure generally have the problem of low efficiency of recalls. Study found that the correct results from some of the link prediction algorithms are complementary, accordingly, the Boosting method was considered to improve it. According to whether there is a link re-lationship between the nodes, the problem was divided into two categories, thus the link prediction algorithm as a two classification problem was defined. Furthermore, the algorithm complementary principle to select a number of represent-ative link prediction algorithms as weak classifiers was followed, and a novel link prediction algorithm based on the AdaBoost algorithm was come up. The experimental results on the data from real dataset like the arXiv paper cooperation network and E-mail network show that, the novel algorithm has a better accuracy than the current mainstream algorithms.