دانلود مقاله ISI انگلیسی شماره 46145
عنوان فارسی مقاله

الگوریتم مبتنی کلونی مورچه برای همپوشانی تشخیص جامعه در شبکه های پیچیده

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46145 2015 13 صفحه PDF سفارش دهید 7550 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
An ant colony based algorithm for overlapping community detection in complex networks
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Physica A: Statistical Mechanics and its Applications, Volume 427, 1 June 2015, Pages 289–301

کلمات کلیدی
الگوریتم کلونی مورچه - همپوشانی تشخیص جامعه - اطلاعات اکتشافی - شبکه های پیچیده
پیش نمایش مقاله
پیش نمایش مقاله الگوریتم مبتنی کلونی مورچه برای همپوشانی تشخیص جامعه در شبکه های پیچیده

چکیده انگلیسی

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants’ location initialization, ants’ movement and post processing phases. An ants’ location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants’ movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

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