کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7376133 | 1480078 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Attributed community mining using joint general non-negative matrix factorization with graph Laplacian
ترجمه فارسی عنوان
معادن متعلق به جامعه با استفاده از تقسیم مشترک ماتریس غیر منفی مشترک با لاپلایک گراف
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
چکیده انگلیسی
Community mining for complex social networks with link and attribute information plays an important role according to different application needs. In this paper, based on our proposed general non-negative matrix factorization (GNMF) algorithm without dimension matching constraints in our previous work, we propose the joint GNMF with graph Laplacian (LJGNMF) to implement community mining of complex social networks with link and attribute information according to different application needs. Theoretical derivation result shows that the proposed LJGNMF is fully compatible with previous methods of integrating traditional NMF and symmetric NMF. In addition, experimental results show that the proposed LJGNMF can meet the needs of different community minings by adjusting its parameters, and the effect is better than traditional NMF in the community vertices attributes entropy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 495, 1 April 2018, Pages 324-335
Journal: Physica A: Statistical Mechanics and its Applications - Volume 495, 1 April 2018, Pages 324-335
نویسندگان
Zigang Chen, Lixiang Li, Haipeng Peng, Yuhong Liu, Yixian Yang,