【摘要】 针对现有在线社交网络用户分类方法不能有效利用用户属性和关系网络信息提高分类性能的问题,设计了一种基于随机游走模型的多标签分类方法MLCMRW。该方法的分类过程包括学习用户初始化类别标签以及通过迭代推理获得用户稳定标签分布两个阶段,并且其可以同时考虑用户属性以及关系网络特征信息进行分类。多个在线社交网络数据集上进行的实验表明,MLCMRW比其它已有的代表性方法有更好的分类性能,并且更适合对现实中的在线社交网络进行用户分类。更多还原

【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 classification 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.更多还原