网络异构信息的张量分解聚类方法

提出基于张量分解的聚类算法，能够同时处理网络中多类型、多语义关系的异构信息。网络信息体系中的各种异构信息被建模为一个多维张量，异构信息之间丰富的语义关系建模为张量中的元素。提出有效的张量分解方法，将不同类型的信息对象一次性划分到不同的簇中。在人工合成的数据集和真实数据集上的实验结果表明：该聚类方法可以很好地处理网络信息体系中的异构信息聚类问题，并且性能优于现有的聚类方法。
A tensor decomposition based clustering method was proposed for heterogeneous information in networks. This clustering method can cluster multiple types of objects and rich semantic relationships simultaneously. The multi-types of information objects in networks were modeled as a high-dimensional tensor, and the rich semantic relationships among different types of objects were modeled as elements in the tensor. Based on an effective tensor decomposition method, the multi-types of objects were partitioned into different clusters simultaneously. The experimental results on both synthetic datasets and real-world dataset show that the proposed clustering method can deal with the heterogeneous information in networks well, and can outperform the state-of-the-art clustering algorithms.