基于空间尺度粗粒化的异常检测方法

目前，大部分基于链路预测对社会网络进行异常检测的研究中，缺乏对异常节点演化影响的分析，且受社会网络规模以及复杂度的限制，检测效率普遍不高。针对上述问题，提出了一种基于空间尺度粗粒化和异常节点加权机制的异常检测方法。首先利用凝聚型社区发现算法Louvain对社会网络进行粗粒化得到简化网络，然后在简化网络的演化过程中识别有异常演化行为的节点，并将其异常演化过程量化，引入异常节点加权机制到链路预测方法中进行异常检测。在真实社会网络数据集VAST、Email-EU（dept1和dept2）以及Enron上，与基于LinkEvent的不同调整策略算法和NESO_ED方法进行对比。结果表明，该方法可以兼顾异常检测的稳定性和敏感性，能够更合理地描述网络演化过程，得到更好的异常检测效果。

Abnormal detection method based on spatial scale coarse-grained

At present, most link-prediction based models on anomaly detection in social networks lack the ability to consider the influence of abnormal nodes evolution. With the limitation of network scale and complexity, the detection efficiency of traditional models is generally low. To address these issues, this paper proposed an anomaly detection method based on the spatial scale coarse granulation and weighting mechanism on abnormal nodes. Firstly, the method introduced a cohesive community discovery algorithm, Louvain algorithm, to coarsely granulate process to streamline network. Subsequently, it identified abnormal nodes with different evolution behaviors in the processed network following the quantification of abnormal evolution process. Finally, it applied the link prediction method combined with a weighting mechanism of abnormal nodes for final abnormal detection. Compared with different LinkEvent-based strategy adjustment algorithms and NESO_ED method on three real social network data sets VAST, Email-EU(dept1 and dept2) and Enron, the proposed method outperforms other state-of-the-art methods, can take into account both stability and sensitivity on anomaly detection tasks and more reasonably describe the network evolution process.