Computer Science ›› 2015, Vol. 42 ›› Issue (2): 198-203.doi: 10.11896/j.issn.1002-137X.2015.02.042

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User Classification Method in Online Social Network Using Random Walks

HE Chao-bo, YANG Zhen-xiong, HONG Shao-wen, TANG Yong, CHEN Guo-hua and ZHENG Kai   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Aiming at the problem that the existing methods for user classification in online social network (OSN) are not enough effective to utilize both attribute and linkage information of user to improve the classification performance,we designed a new multi-label classification method using random walks (MLCMRW) to solve the problem of user cla-ssification in OSN.MLCMRW can utilize both user attribute and linkage information to improve the classification performance.In particular,MLCMRW includes two key parts:learning the initial label distribution and iterative inference for steady label distribution of every user. The experiments on the real-world OSN datasets show that MLCMRW performs quite well than other representative methods.Moreover, it is suitable to classify users in the real-world OSN.

Key words: Online social network,User classification,Random walks

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