III: Small: Dynamic Social Network Mining: Feature Extraction, Modeling and Anomaly Detection

该项目开发了一个通用框架，用于在动态社交网络中进行异常检测，这些网络在链接和节点中都有发展。该框架首先捕获动态网络的时间戳信息，将其转换为精心选择的图核特征空间。然后设计了一种动态建模方法来学习目标动态社会网络的动态性。最后提出了挖掘动态网络中异常节点的异常检测方法。这些方法的主要创新是通过属性包来表示动态网络，包括图核特征来概括每个时间戳中的细节，学习的网络动态潜在变量和用户指定的特征来将属性表示的方向转向目标任务。在此基础上，设计了基于潜在支持向量机和迁移学习的异常节点检测方法。这项研究提供了对进化模式的清晰理解，包括动态社会网络中的正常和异常。本项目开发的方法有助于识别我们生活中的各种异常情况，包括检测网站中的垃圾邮件发送者，监控犯罪网络中的潜在危险活动，识别疾病网络中的恶意感染源等等。这种网络动态的成功建模可以为人际关系的出现和消失提供科学依据，为揭示社交网络过程中潜在的未被发现的进化模式提供前景，并有助于开发更多应用的定性和定量算法。

This project develops a general framework for anomaly detection in dynamic social networks that evolve in both links and nodes. The framework includes first capturing the information of timestamps of dynamic networks by transferring them into carefully selected graph kernel feature spaces. A dynamic modeling method is then designed to learn the dynamism on the target dynamic social network. Anomaly detection methods are finally developed to mine abnormal nodes in the dynamic network. The main innovation of the approaches is to represent dynamic networks by bags of attributes, including graph kernel features to epitome details in each timestamp, learned latent variables for dynamism of networks and user specified features to turn the direction of attributes representation towards aimed tasks. Based on this representation, the project designs innovative methods based on latent support vector machine and transfer learning to detect abnormal nodes. This research provides a clear understanding of evolution patterns, including both normality and abnormality in dynamic social networks. The approaches developed in this project help identify various abnormalities in our life, including detecting spammers in websites, monitoring potential dangerous activities in crime networks, identifying malicious source of infection in disease networks, and many others. The successful modeling of such network dynamics can provide scientific basis for appearance and disappearance of human relationships, improve the prospects for uncovering potentially undiscovered evolution patterns in social networking process and help develop qualitative and quantitative algorithms for more applications.