کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7376135 | 1480077 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mixture models with entropy regularization for community detection in networks
ترجمه فارسی عنوان
مدل های مخلوط با تنظیم آنتروپی برای تشخیص جامعه در شبکه ها
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کلمات کلیدی
00-01، 99-00، شبکه های پیچیده تشخیص جامعه، مدل های مخلوط، آنتروپی،
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
چکیده انگلیسی
Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 496, 15 April 2018, Pages 339-350
Journal: Physica A: Statistical Mechanics and its Applications - Volume 496, 15 April 2018, Pages 339-350
نویسندگان
Zhenhai Chang, Xianjun Yin, Caiyan Jia, Xiaoyang Wang,