LiterMiner: An Academic Network Mining System
This paper addresses several key issues in the LiterMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Integrating the publication data into the academic network from existing article records; 2) Detecting and visualizing communities and evolution in the network; and 3) Analyzing the network multi-dimensionally. We provide a preprocessing framework to deal with the name ambiguity problem in data integration. Furthermore, we provide multiple algorithms to detect communities, and visualize both the communities and the evolution of networks with user friendly interface. Besides, multi-dimensional analyses of related networks have been provided based on the implementation and extension of the GraphOLAP framework. In this paper, we describe the architecture and main features of the system. We also present a case study with real data sets to show these features.
data mining complex network GraphOLAP community structure
Wu Bin Zhao Bin Tian Hongqiao Wen Wanting
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications, Beijing, China 100876
国际会议
西安
英文
800-803
2010-08-07（万方平台首次上网日期，不代表论文的发表时间）