Abstract: Existing static link prediction methods have mostly adopted overlay network to represent social network and used structural information of inter-link to predict future link occurrences. However, these methods can only predict new link occurrences, the repeated old link occurrences are not generally studied, so do not apply to many application domains that the prediction of the repeated link occurrences are of main interest. For these deficiencies of static link prediction, this paper introduces the time series link prediction and combines static graph and time series prediction to obtain hybrid time series link prediction. Using the Enron email data the experiments confirm that the time series link prediction can achieve better prediction performance than static link prediction. Furthermore, the hybrid link prediction can get better performance than only using static or time series link prediction.

Key words: link prediction, time series, autoregressive integrated moving average (ARIMA) model

摘要： 已有静态链接预测主要采用覆盖图表示社会网络, 利用链接之间的结构信息来预测链接的发生。然而, 这些方法仅能预测新链接的发生, 而对旧链接的重复发生没有做预测, 因此不适合预测重复发生的链接是主要兴趣的应用领域。针对静态链接预测算法的不足, 引入时间序列链接预测算法, 并且组合静态和时间序列链接预测算法为混合时间序列链接预测算法。在Enron电子邮件数据集上的实验结果表明, 时间序列链接预测算法性能优于静态链接预测, 混合时间序列链接预测算法的预测性能比单独使用静态或时间序列链接预测算法都要优越。

关键词: 链接预测, 时间序列, AKIMA模型