Network-based naive Bayes model for social network
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Naive Bayes (NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes (NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational effciency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance. A real data example is also analyzed for illustration purpose.
Keywordsclassification naive Bayes Sina Weibo social network data
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This work was supported by National Natural Science Foundation of China (Grant Nos. 11701560, 11501093, 11631003, 11690012, 71532001, 11525101), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (Grant No. 16XNLF01), the Beijing Municipal Social Science Foundation (Grant No. 17GLC051), Fund for Building World-Class Universities (Disciplines) of Renmin University of China, the Fundamental Research Funds for the Central Universities (Grant Nos. 130028613, 130028729 and 2412017FZ030), China’s National Key Research Special Program (Grant No. 2016YFC0207700) and Center for Statistical Science at Peking University.
- 7.Craven M, McCallum A, PiPasquo D, et al. Learning to extract symbolic knowledge from the World Wide Web. In: Proceedings of the 15th National Conference on Artificial Intelligence. World Wide Web Internet and Web Information Systems, vol. 118. Menlo Park: Amer Assoc Artif Intell, 1998, 509–516Google Scholar
- 17.Lewis D D. Evaluating and optimizing autonomous text classification systems. In: International Acm Sigir Conference on Research and Development in Information Retrieval. New York: ACM, 1995, 246–254Google Scholar
- 19.Macskassy S A, Provost F. Classification in networked data: A toolkit and a univariate case study. J Mach Learn Res, 2007, 8: 935–983Google Scholar
- 21.Neville J, Jensen D. Iterative classification in relational data. In: Proceedings of American Association for Artificial Intelligence Workshop on Learning Statistical Models from Relational Data. Palo Alto: AAAI Press, 2000, 42–49Google Scholar