Abstract
Link prediction has recently received considerable attention in signed networks. Most of the existing methods assume that the signed network topology is certain, such as network structure, entities relations and entities attributes. However, the assumption is not applicable, since the signed network is uncertain. As a result, the prediction accuracy cannot be ensured if the uncertainty of the signed networks is ignored. In this paper, we regard the signed network as an identical-discrepancy-contrary system employing the set pair theory, and propose a new link prediction measure SNCD which integrates both the certain and uncertain relations, local and global information at the same time. After a series of experiment, the experimental results show that our proposed method provides better prediction accuracy and correctness.
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Acknowledgements
This work is supported by the National Youth Science Foundation of Hebei (No. F2017209070), the National Science Foundation of China, (No. 61472340, No. 61303017), the National Youth Science Foundation of China (No. 61602401), the Natural Science Foundation of Hebei Province (No. F2014210068), and the Fourth Outstanding Youth Foundation of Shijiazhuang Tiedao University.
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Chen, X., Guo, JF., Pan, X. et al. Link prediction in signed networks based on connection degree. J Ambient Intell Human Comput 10, 1747–1757 (2019). https://doi.org/10.1007/s12652-017-0613-2
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DOI: https://doi.org/10.1007/s12652-017-0613-2