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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/14215
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| タイトル: | Combined node and link partitions method for finding overlapping communities in complex networks |
| 著者: | Jin, Di
Gabrys, Bogdan
Dang, Jianwu |
| キーワード: | Complex Network
Commmunity detecltion |
| 発行日: | 2015-02-26 |
| 出版者: | Nature Publishing Group |
| 誌名: | Scientific Reports |
| 巻: | 5 |
| 開始ページ: | Article number: 8600 |
| DOI: | 10.1038/srep08600 |
| 抄録: | Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures. |
| Rights: | Di Jin, Bogdan Gabrys, and Jianwu Dang, Scientific Reports, 5, 2015, Article number: 8600. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| URI: | http://hdl.handle.net/10119/14215 |
| 資料タイプ: | publisher |
| 出現コレクション: | b10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
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記述 |
サイズ | 形式 |
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| 21961.pdf | | 692Kb | Adobe PDF | 見る/開く |
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