کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7374657 1480061 2018 28 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A framework of community detection based on individual labels in attribute networks
ترجمه فارسی عنوان
چارچوب تشخیص جامعه براساس برچسب های فردی در شبکه های صفت
کلمات کلیدی
تشخیص جامعه، شبکه اختصاصی برچسب فردی، تقسیم ماتریس غیر منفی،
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات فیزیک ریاضی
چکیده انگلیسی
Community detection is an important problem for understanding the structure and function of complex networks and has attracted a lot of attention in recent decades. Most community detection algorithms only focus on the topology of networks. However, there is still much valuable information hidden in the networks, such as the attributes or content of the nodes and the useful prior information. Obviously, taking full advantage of these resources can improve the effectiveness of community detection. In this paper, we present a semi-supervised community detection framework named SCDAN (Semi-supervised Community Detection in Attribute Networks), in which a non-negative matrix factorization model is utilized to effectively integrate network topology, node attributes and individual labels simultaneously. The comparative experiments on real-world networks show that SCDAN significantly improves the performance of community detection and provides semantic interpretation of communities.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 512, 15 December 2018, Pages 523-536
نویسندگان
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