计算机科学 ›› 2015, Vol. 42 ›› Issue (2): 198-203.doi: 10.11896/j.issn.1002-137X.2015.02.042
贺超波,杨镇雄,洪少文,汤庸,陈国华,郑凯
HE Chao-bo, YANG Zhen-xiong, HONG Shao-wen, TANG Yong, CHEN Guo-hua and ZHENG Kai
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。
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