基于深度稀疏自动编码器的社区发现算法
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TP311

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国家自然科学基金(61373023)


Community Detection Algorithm Based on Deep Sparse Autoencoder
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    摘要:

    社区结构是复杂网络的重要特征之一,社区发现对研究网络结构有重要的应用价值.k-均值等经典聚类算法是解决社区发现问题的一类基本方法.然而,在处理网络的高维矩阵时,使用这些经典聚类方法得到的社区往往不够准确.提出一种基于深度稀疏自动编码器的社区发现算法CoDDA(a community detection algorithm based on deep sparse autoencoder),尝试提高使用这些经典方法处理高维邻接矩阵进行社区发现的准确性.首先,提出基于跳数的处理方法,对稀疏的邻接矩阵进行优化处理,得到的相似度矩阵不仅能够反映网络拓扑结构中相连节点间的相似关系,同时还反映了不相连节点间的相似关系.然后,基于无监督深度学习方法构建深度稀疏自动编码器,对相似度矩阵进行特征提取,得到低维的特征矩阵.与邻接矩阵相比,特征矩阵对网络拓扑结构有更强的特征表达能力.最后,使用k-均值算法对低维特征矩阵聚类得到社区结构.实验结果显示:与6种典型的社区发现算法相比,CoDDA算法能够发现更准确的社区结构.同时,参数实验结果显示,CoDDA算法发现的社区结构比直接使用高维邻接矩阵的基本k-均值算法发现的社区结构更为准确.

    Abstract:

    Community structure is one of the most important features of complex network. Community detection is of great significance in exploring the network structure. Classical clustering algorithms such as k-means are the basic methods for community detection. However, the detection results are often not accurate enough when dealing with high-dimensional matrix when using these classical methods. In this study, a community detection algorithm based on deep sparse autoencoder (CoDDA) is proposed to improve the accuracy of community detection using high-dimensional adjacent matrix with the classical methods. First, a hop-based operation for sparse adjacent matrix is provided to obtain the similarity matrix, which can express not only the relations between nodes that are linked but also the relations between nodes that are not linked. Then, a deep sparse autoencoder based on unsupervised deep learning methods is designed to extract the features of similarity matrix and obtain the low-dimensional feature matrix which can represent the features of network topology better than similarity matrix. Finally, k-means is used to identify the communities according to the feature matrix. Experimental results show that CoDDA can obtain more accurate communities than the six baseline methods. Besides, the parameter analysis indicates that CoDDA can result in more accurate communities than the k-means algorithm which finds the communities according to the high-dimensional matrix directly.

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尚敬文,王朝坤,辛欣,应翔.基于深度稀疏自动编码器的社区发现算法.软件学报,2017,28(3):648-662

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历史
  • 收稿日期:2016-07-31
  • 最后修改日期:2016-09-14
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  • 在线发布日期: 2018-06-06
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