Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along *** are common in multimedia *** detection is one of the essential tasks in analyzing these networks th...
详细信息
Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along *** are common in multimedia *** detection is one of the essential tasks in analyzing these networks though it is not well *** this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic *** conclude features of rare categories and two types of anomalies of rare *** we present a novel rare category detection method,called DIRAD,to detect rare category candidates with *** develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex ***,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.
A dynamic network refers to a graph structure whose nodes and/or links dynamically change over *** visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the ...
详细信息
A dynamic network refers to a graph structure whose nodes and/or links dynamically change over *** visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network *** work focuses on detecting anomalous changing patterns in the dynamic network,the rare occurrence of which could damage the development of the entire *** this study,we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic *** proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the *** particular,a novel visualization is introduced,which represents the snapshots of a dynamic network in a series of connected triangular *** clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding *** evaluate our technique via a case study and a user *** evaluation results verify the effectiveness of our system.
暂无评论