Abstract
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.
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Acknowledgement
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.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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He, K., Guan, D., Yuan, W. (2018). Community Preserving Sign Prediction for Weak Ties of Complex Networks. In: Wang, L., Qiu, T., Zhao, W. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-78078-8_2
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DOI: https://doi.org/10.1007/978-3-319-78078-8_2
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