[1]WANG Runfang,CHEN Zengqiang,LIU Zhongxin.Link prediction in complex networks with syncretic naive Bayes methods[J].CAAI Transactions on Intelligent Systems,2019,14(1):99-107.[doi:10.11992/tis.201810025]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 1
Page number:
99-107
Column:
学术论文—智能系统
Public date:
2019-01-05
- Title:
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Link prediction in complex networks with syncretic naive Bayes methods
- Author(s):
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WANG Runfang1; CHEN Zengqiang1; 2; LIU Zhongxin1; 2
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1. College of Artificial Intelligence, Nankai University, Tianjin 300350, China;
2. Key Laboratory of Intelligent Robotics of Tianjin, Tianjin 300350, China
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- Keywords:
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complex network; syncretic naive Bayes model; local naive Bayes model; Bayes model; link prediction; common neighbors; the degree of node; network reconstruction
- CLC:
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TP391
- DOI:
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10.11992/tis.201810025
- Abstract:
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Recently, complex networks have become a research hotspot. However, edge information in the real network is incomplete, which is not conducive to the analysis and research of the network. Link prediction can provide a fundamental basis for network reconstruction by digging out the missing edges in the network. This paper demonstrates that the generation of links in the network is not only influenced by external factors (common neighbors) but also by its own factors. Among them, the influence of common neighbors can be quantified via the local naive Bayes (LNB) model in the literature, whereas the influence of nodes can be quantified depending on their degree. Therefore, a syncretic naive Bayes (SNB) model is proposed based on comprehensive consideration of the influence of the two abovementioned aspects. The model is then extended to common neighbors, Adamic-Adar, and Resource Allocation methods. Finally, the experimental results on USAir show that the prediction accuracy of the method is higher than that of LNB and the benchmark method, which proves the effectiveness of the SNB model.