移动社交网络异常签到在线检测算法

随着智能手机、Pad等智能移动设备的广泛普及，移动社交网络的应用得到了快速发展。本文针对移动社交网络中用户异常签到位置检测问题，提出了一类基于用户移动行为特征的异常签到在线检测方法。首先，在基于距离的异常模型基础上，提出了基于历史位置（H-Outlier）和基于好友圈（F-Outlier）两种异常签到模型；然后，针对H-Outlier提出了一种优化的检测算法H-Opt，利用所提的签到状态模型与优化的邻居搜索机制降低检测时间；针对F-Outlier提出了一种基于触发的优化检测算法F-Opt，将连续的在线异常检测转化成了基于触发的异常检测方式；最后，在真实的移动社交网络用户签到数据集上，验证了所提算法的有效性。实验结果显示，F-Opt显著降低了H-Opt的异常检测错误率；同时，相比于LUE算法，F-Opt和H-Opt的效率分别平均提升了2.34倍和2.45倍。

Online check-in outlier detection method in mobile social networks

With the increasing popularization of smartphone, Pads and other smart mobile devices, the use of mobile social networks has also developed rapidly. In this paper, we propose an online method for detecting check-in outliers based on user mobility behavior in mobile social networks. First, based on a distance-based outlier model, we propose two check-in outlier models with respect to historical location (H-Outlier) and friend circle (F-Outlier), respectively. Second, for the H-Outlier, we propose an optimized detection algorithm called H-Opt, which utilizes the proposed check-in status model and an optimized neighbor searching mechanism to reduce computation time. For the F-Outlier, we propose a trigger-based optimized detection algorithm called F-Opt, which transforms continuous online outlier detection into trigger-based outlier detection. Lastly, we present our experimental results, based on a real-world check-in dataset, which demonstrate the effectiveness of the proposed algorithm. Our experimental results show that F-Opt significantly reduces the error rate of H-Opt outlier detection. In addition, compared with the LUE algorithm, the F-Opt and H-Opt algorithms improved efficiency by 2.34 and 2.45 times, respectively.