基于多语义元路径的异质网节点分类方法*

异质网是包含多种类型的对象和复杂链接关系的大规模异构信息网络.针对科研异质网,文中提出基于元路径信息的节点分类方法.利用异质网中具有不同语义信息的元路径,建立节点之间的关联特征矩阵,并通过加入跳转路径扩展异质网,解决信息稀疏问题.在DBLP数据集上的实验表明,文中方法可以有效利用较少的分类标签,解决复杂网络中的节点分类,在标注数据比例规模较小时,加入跳转路径,优化决策树分类性能.

Multi-semantic Metapath Based Classification Method in Heterogeneous Information Network

Heterogeneous information network(HIN) is a kind of large-scale network containing many types of objects and complex links. The metapath based object classification method in HIN is proposed in this paper. The correlation feature matrix between nodes is built by the use of the metapath with different semantic information. In addition, the jumping path is extended to solve the problem of information sparseness. The experiments are conducted on DBLP dataset and the results show high performance of the proposed method in the complex network by using fewer labeled data. Furthermore, t-test result denotes that the performance is improved significantly by jumping path with small labeled data.