| 引用本文: | 吴继冰,黄宏斌,邓苏.网络异构信息的张量分解聚类方法.[J].国防科技大学学报,2018,40(5):146-152,170.[点击复制] |
| WU Jibing,HUANG Hongbin,DENG Su.Tensor decomposition based clustering method for heterogeneous information in networks[J].Journal of National University of Defense Technology,2018,40(5):146-152,170[点击复制] |
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| 网络异构信息的张量分解聚类方法 |
| 吴继冰, 黄宏斌, 邓苏 |
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(国防科技大学 系统工程学院 信息系统工程重点实验室， 湖南 长沙 410073)
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| 摘要: |
| 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。 |
| 关键词: 聚类 异构信息 张量分解 信息网络 |
| DOI：10.11887/j.cn.201805022 |
| 投稿日期：2017-06-02 |
| 基金项目:国家自然科学基金资助项目（61401482， 61401483） |
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| Tensor decomposition based clustering method for heterogeneous information in networks |
| WU Jibing, HUANG Hongbin, DENG Su |
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(Science and Technology on Information Systems Engineering Laboratory, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| 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. |
| Keywords: clustering heterogeneous information tensor decomposition information networks |
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