异质网络中融合多类信息的链路情感倾向预测

信息网络中基于节点间情感关系分析的链路情感倾向预测在商业营销、内容推荐等领域应用广泛，是网络分析的一个研究重点.传统的链路情感倾向预测方法对于数据信息的挖掘不够充分，忽略了对数据深层语义以及节点属性等信息的利用，预测准确度有待提升.针对以上问题，提出了异质网络中融合多种类型信息的链路情感倾向预测模型.模型首先引入预测基值作为特定节点间情感关系的粗略评估，然后结合节点的相似关系以及节点的属性等信息完成预测.其中，在捕获网络中具有相似情感倾向的节点用于预测任务时，提出了一种基于限制路径类型元路径的遍历游走方法.在5个公共数据集上的实验结果验证了所提模型的有效性及对于稀疏矩阵、冷启动问题的处理能力，并揭示了模型各组成部分在预测过程中的作用.

Prediction of links’ sentiment tendency by fusing multi-information in heterogeneous networks

Prediction of links’ sentiment tendency based on the analysis of the emotional relationship between nodes in information networks is widely used in commercial marketing, content recommendation, and other fields, which is a research focus of network analysis. The traditional studies on the prediction of links’ sentiment didn’t mine the information in data sufficiently. They often ignore the deep semantics of data or the node attributes and so on. Therefore, the accuracy of prediction can be further improved. For the above problems, we propose a model for predicting links’ sentiment tendency in this paper, which integrates multiple types of information in heterogeneous networks. The model introduces a basic estimate as a rough evaluation of the emotional relationship between specific nodes and combines information from similar nodes as well as nodes’ attributes to complete the prediction task. When capturing nodes with similar sentiment tendencies in the network for prediction tasks, a traversal walk method based on meta-path with restricted path types was proposed. Extensive experiments on five real-world datasets demonstrate the effectiveness of the proposed model. Moreover, the experimental results show the capability of the proposed model to deal with sparse matrix and cold-start problems and reveal the efficacy of each component of the model throughout the prediction process.