Community discovery in networks with deep sparse filtering
Community discovery in networks with deep sparse filtering
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DOI：
10.1016/j.patcog.2018.03.026
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Yu Xie;Maoguo Gong;Shanfeng Wang;Bin Yu
中科院分区：
文献类型：
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作者：
Yu Xie;Maoguo Gong;Shanfeng Wang;Bin Yu
在过去的十年中，网络社区发现引起了很多研究者的关注，而社区结构是复杂网络中最重要的性质之一。提出了一种基于深度稀疏滤波的网络社区发现方法。网络的特征是通过稀疏滤波（一种无监督深度学习算法）从网络的有效表示中提取的。因此，提取的特征被用来划分网络。在人工和真实网络数据集上的实验结果表明，该算法尤其是基于Serrensen-Dice相似性矩阵表示的算法是有效的，在发现社区结构方面优于现有的几种算法.
In the past decade, network community discovery has attracted great attention from quite a few researchers, and community structure is one of the most significant properties in complex networks. This paper presents a novel method for network community discovery based on deep sparse filtering. The features of the network are extracted by sparse filtering, an unsupervised deep learning algorithm, from an efficient representation of the network. Consequently, extracted features are employed to partition the network. Experiment results on both synthetic and real-world network datasets indicate that the proposed algorithm especially based on S⌀ rensen–Dice’s similarity matrix representation of the network is efficient and it outperforms several state-of-art algorithms in discovering community structure.