归因网络中的群体与异常Communities and Anomalies in Attributed Networks |
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课程网址: | https://videolectures.net/videos/kdd2016_akoglu_attributed_networ... |
主讲教师: | Leman Akoglu |
开课单位: | KDD 2016研讨会 |
开课时间: | 2016-10-12 |
课程语种: | 英语 |
中文简介: | 给定一个节点与一系列属性相关联的网络,我们如何定义和描述社区?我们如何发现异常社区和社区内的异常?长期以来,人们一直在研究网络,最近的重点转向了“有内容的网络”。对于此类网络,重新考虑了长期研究的网络问题,如排名、聚类和相似性,因为节点/边缘属性和类型等新信息有助于丰富公式,增加我们对现实世界网络的理解。在本次演讲中,我将介绍我们在具有节点属性的网络中发现异常的工作。我们在属性网络中进行异常挖掘的主要方法是通过社区。特别是,我们量化了社区可以通过其成员“点击”的属性(子集)来表征的程度。然后,我们使用这样一个量作为“正态性”得分,在此基础上,我们识别社区内的单个异常节点,以及由于其低正态性而作为一组节点异常的社区。 |
课程简介: | Given a network in which nodes are associated with a list of attributes, how can we define and characterize communities? How can we spot anomalous communities and anomalies within communities? Networks have long been studied and focus has most recently shifted to 'networks with content'. Long-studied network questions, such as ranking, clustering, and similarity, are reconsidered for such networks, as the new information such as node/edge attributes and types help enrich the formulations and increase our understanding of real-world networks. In this talk, I will introduce our work on spotting anomalies in networks with node attributes. Our main approach to anomaly mining in attributed networks is through communities. In particular, we quantify the degree that a community can be characterized through (a subset of) attributes on which its members 'click'. We then use such a quantity as a 'normality' score, based on which we identify individual anomalous nodes inside communities as well as communities that are anomalous as a group of nodes due to their low normality. |
关 键 词: | 归因网络; 网络问题; 正态性 |
课程来源: | 视频讲座网 |
数据采集: | 2025-01-07:liyq |
最后编审: | 2025-01-07:liyq |
阅读次数: | 12 |