Community Preserving Sign Prediction for Weak Ties of Complex Networks

  • Kangya He
  • Donghai Guan
  • Weiwei Yuan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)


The weak ties are crucial bridges between the tightly coupled node groups in complex networks. Despite of their importance, no existing work has focused on the sign prediction of weak ties. A community preserving sign prediction model is therefore proposed to predict the sign of the weak ties. Nodes are firstly divided into different communities. The weak ties are then detected via the connections of the divided communities. SVM classifier is finally trained and used to predict the sign of weak ties. Experiments held on the real world dataset verify the high prediction performances of our proposed method for weak ties of complex networks.


Sign prediction Weak tie Link prediction Signed network 



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). This work was also supported by Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China (Grant No. CAAC-ITRB-201501 and Grant No. CAAC-ITRB-201602). Dr. Weiwei Yuan is the corresponding author of this paper.


  1. 1.
    Yuan, W., Guan, D., Lee, Y.K., et al.: Improved trust-aware recommender system using small-worldness of trust networks. J. Knowl. Based Syst. 23(3), 232–238 (1981)CrossRefGoogle Scholar
  2. 2.
    Wei, L., Xu, H., Wang, Z., et al.: Topic detection based on weak tie analysis: a case study of LIS research. J. Data Inf. Sci. 1(4), 81–101 (2016)Google Scholar
  3. 3.
    Li, X., Fang, H., Zhang, J.: Rethinking the link prediction problem in signed social networks. In: AAAI, pp. 4955–4956 (2017)Google Scholar
  4. 4.
    Tang, J., Chang, Y., Aggarwal, C., et al.: A survey of signed network mining in social media. J. ACM Comput. Surv. (CSUR) 49(3), 42 (2016)Google Scholar
  5. 5.
    Si, C., Jiao, L., Wu, J., et al.: A group evolving-based framework with perturbations for link prediction. J. Physica A Stat. Mech. Appl. 475, 117–128 (2017)CrossRefGoogle Scholar
  6. 6.
    Martnez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. J. ACM Comput. Surv. (CSUR) 49(4), 69 (2016)Google Scholar
  7. 7.
    Nocaj, A., Ortmann, M., Brandes, U.: Adaptive disentanglement based on local clustering in small-world network visualization. J. IEEE Trans. Vis. Comput. Graph. 22(6), 1662–1671 (2016)CrossRefGoogle Scholar
  8. 8.
    Leicht, E.A., Newman, M.E.J.: Community structure in directed networks. J. Phys. Rev. Lett. 100(11), 118703 (2008)CrossRefGoogle Scholar
  9. 9.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: ACM Conference on Recommender Systems, pp. 17–24. ACM (2007)Google Scholar
  10. 10.
    Khodadadi, A., Jalili, M.: Sign prediction in social networks based on tendency rate of equivalent micro-structures. J. Neurocomput. 257, 175–184 (2017)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

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

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