中文摘要：

为提高符号网络的连边符号预测准确率,深入分析了影响连边符号的各项基本机理,拓展了＂结构平衡理论＂和＂地位理论＂,同时将网页网络中的＂PageTrust＂度量引入符号网络用以刻画符号网络中节点的重要性.在融合从不同角度反映连边符号形成机制理论的基础上,抽取出一组最能反映连边正负的网络特征,并将这类网络特征用于2类机器学习模型的训练与测试.2个真实网络数据集上的实验结果表明,训练所得模型具有较已有模型更高的预测准确率和更好的通用性.

英文摘要：

In order to make the link sign prediction more accurate in signed networks, it is necessary to analyse each underlying principle of generating signed networks. Structure balance theory and status theo- ry are extended to gain more information for link sign prediction. A new measurement named PageTrust in web network is introduced to describe the importance of node of signed networks. On the basis of integra- ting different kind principles of generating signed networks, a group of refined features are extracted. Based on those creative features, two link sign predictors using supervised machine learning algorithms are established. Experimental results on two real signed networks demonstrate that learned model is more accurate and generalized than other state-of-the-art methods.