کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408879 | 679047 | 2008 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures](/preview/png/408879.png)
چکیده انگلیسی
Identifying community structure in complex networks is closely related to clustering of data in other areas without an underlying network structure. In this paper, we propose a nonnegative matrix factorization (NMF)-based method for finding community structure. We first evaluate several similarity measures, such as diffusion kernel similarity, shortest path based similarity on several widely well-studied networks. Then, we apply NMF with diffusion kernel similarity to a large biological network, which demonstrates that our method can find biologically meaningful functional modules. Comparison with other algorithms also indicates the good performance of our method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 72, Issues 1–3, December 2008, Pages 134–141
Journal: Neurocomputing - Volume 72, Issues 1–3, December 2008, Pages 134–141
نویسندگان
Rui-Sheng Wang, Shihua Zhang, Yong Wang, Xiang-Sun Zhang, Luonan Chen,