网络异构信息的张量分解聚类方法

Tensor decomposition based clustering method for heterogeneous information in networks

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.