基于半监督学习的社交网络用户属性预测

Predicting users' profiles in social network based on semi-supervised learning

研究如何利用社交关系推测用户的隐藏属性(私隐信息),采用基于图的半监督学习方法推测用户属性。为了提高预测的准确率,提出利用属性聚集度评价属性推测的难易程度,并依用户节点标记的不同,设计不同的权重公式计算用户之间的关系强度。以"人人网"数据作为实验数据,对用户的兴趣与毕业学校进行预测,验证了方法的有效性。

How to derive the users＇ hidden profiles using social relationships is studied. Considering the network structure of social network and characteristics of users＇ data, the graph based semi-supervised learning algorithm is chose to predict users＇ profiles. To improve the prediction accuracy, the attribute affinity is proposed to evaluate whether the value of an attribute is easy to be predicated, and different weight computing formulas are designed to calculate the relationship between users. The experimental data is collected from ＂renren network＂ and two attributes, hobbies and schools, are predicted in the experiments. The experimental results show that the strategies for computing weights among users are effective.