Top-K structural diversity search in large networks

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• 作者：Xin Huang ; Hong Cheng ; Rong-Hua Li ; Lu Qin ; Jeffrey Xu Yu
• 关键词：Structural diversity ; Disjoint ; set forest ; $$\hbox {A}^*$$ A ?search ; Dynamic graph
• 刊名：The VLDB Journal
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：24
• 期：3
• 页码：319-343
• 全文大小：2,069 KB
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• 作者单位：Xin Huang (1)
  Hong Cheng (1)
  Rong-Hua Li (2)
  Lu Qin (3)
  Jeffrey Xu Yu (1)
  
  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, New Territories, Hong Kong
  2. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen University, Shenzhen, China
  3. Quantum Computation and Intelligent Systems, Department of Engineering and Information Technology, University of Technology, Sydney, Australia
  
• 刊物类别：Computer Science
• 刊物主题：Database Management
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：0949-877X

文摘

Social contagion depicts a process of information (e.g., fads, opinions, news) diffusion in the online social networks. A recent study reports that in a social contagion process, the probability of contagion is tightly controlled by the number of connected components in an individual’s neighborhood. Such a number is termed structural diversity of an individual, and it is shown to be a key predictor in the social contagion process. Based on this, a fundamental issue in a social network is to find top-\(k\) users with the highest structural diversities. In this paper, we, for the first time, study the top-\(k\) structural diversity search problem in a large network. Specifically, we study two types of structural diversity measures, namely, component-based structural diversity measure and core-based structural diversity measure. For component-based structural diversity, we develop an effective upper bound of structural diversity for pruning the search space. The upper bound can be incrementally refined in the search process. Based on such upper bound, we propose an efficient framework for top-\(k\) structural diversity search. To further speed up the structural diversity evaluation in the search process, several carefully devised search strategies are proposed. We also design efficient techniques to handle frequent updates in dynamic networks and maintain the top-\(k\) results. We further show how the techniques proposed in component-based structural diversity measure can be extended to handle the core-based structural diversity measure. Extensive experimental studies are conducted in real-world large networks and synthetic graphs, and the results demonstrate the efficiency and effectiveness of the proposed methods.