Tri-Level Cross-Domain Sign Prediction for Complex Network

  • Jiali Pang
  • Donghai Guan
  • Weiwei YuanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)


Sign prediction is a fundamental research issue in complex network mining, while the high cost of data collection leads to insufficient data for prediction. The transfer learning method can use the transferable knowledge in other networks to complete the learning tasks in the target network. However, when the inter-domain differences are large, it is difficult for existing methods to obtain useful transferable knowledge. We therefor propose a tri-level cross-domain model using inter-domain similarity and relativity to solve the sign prediction problem in complex networks (TCSP). The first level pre-classifies the source domain, the second level selects the key instances of the source domain, and the third level calculates the similarity between the source domain and the target domain to obtain the pseudo-labels of the target domain. These “labeled” instances are used to train the sign classifier and predict the sign in the target network. Experimental results on real complex network datasets verify the effectiveness of the proposed method.


Sign prediction Transfer learning Complex networks 



This research was supported by Nature Science Foundation of China (Grant No. 61672284), Natural Science Foundation of Jiangsu Province (Grant No. BK20171418), China Postdoctoral Science Foundation (Grant No. 2016M591841), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Corroborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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