Inferring Diffusion Networks with Sparse Cascades by Structure Transfer

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• 作者：Senzhang Wang (17)
  Honghui Zhang (18)
  Jiawei Zhang (19)
  Xiaoming Zhang (17)
  Philip S. Yu (19) (20)
  Zhoujun Li (17)
  
  17. State Key Laboratory of Software Development Environment ; Beihang University ; Beijing ; China
  18. Department of Computer Science and Technology ; Tsinghua University ; Beijing ; China
  19. Department of Computer Science ; University of Illinois at Chicago ; Chicago ; USA
  20. Institute for Data Science ; Tsinghua University ; Beijing ; China
  
• 关键词：Information diffusion ; Network inference ; Transfer learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9049
• 期：1
• 页码：405-421
• 全文大小：466 KB
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• 作者单位：Database Systems for Advanced Applications
• 丛书名：978-3-319-18119-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Inferring diffusion networks from traces of cascades has been intensively studied to gain a better understanding of information diffusion. Traditional methods normally formulate a generative model to find the network that can generate the cascades with the maximum likelihood. The performance of such methods largely depends on sufficient cascades spreading in the network. In many real-world scenarios, however, the cascades may be rare. The very sparse data make accurately inferring the diffusion network extremely challenging. To address this issue, in this paper we study the problem of transferring structure knowledge from an external diffusion network with sufficient cascade data to help infer the hidden diffusion network with sparse cascades. To this end, we first consider the network inference problem from a new angle: link prediction. This transformation enables us to apply transfer learning techniques to predict the hidden links with the help of a large volume of cascades and observed links in the external network. Meanwhile, to integrate the structure and cascade knowledge of the two networks, we propose a unified optimization framework TrNetInf. We conduct extensive experiments on two real-world datasets: MemeTracker and Aminer. The results demonstrate the effectiveness of the proposed TrNetInf in addressing the network inference problem with insufficient cascades.