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

III：小：动态社交网络挖掘：特征提取、建模和异常检测

• 批准号: 1218393
• 负责人: Jing Gao
• 金额: $ 50万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218393&HistoricalAwards=false
• 关键词: III; Small; Dynamic; Social; Network

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.

这个项目开发了一个在动态社交网络中进行异常检测的通用框架，动态社交网络在链接和节点中都在进化。该框架首先通过将动态网络的时间戳信息转换到精心选择的图核特征空间来捕获这些信息。然后设计了一种动态建模方法来学习目标动态社会网络上的动态性。最后提出了挖掘动态网络中异常节点的异常检测方法。该方法的主要创新是用属性包来表示动态网络，包括用图核特征来概括每个时间戳中的细节，学习网络动态的潜在变量，以及用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转移学习的异常节点检测方法。本研究对动态社会网络的演化模式有了清晰的认识，包括正常模式和异常模式。该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。这种网络动力学的成功建模可以为人类关系的出现和消失提供科学依据，改善发现社交网络过程中潜在的未被发现的进化模式的前景，并有助于开发更多应用的定性和定量算法。