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Sparse Nonnegative Matrix Factorization for Multiple Local Community Detection
arXiv - CS - Social and Information Networks Pub Date : 2020-01-20 , DOI: arxiv-2001.06951
Dany Kamuhanda, Meng Wang and Kun He

Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such group of nodes are densely connected or have a high probability of being connected internally than their connections to other clusters in the network. Existing local community detection methods focus on finding either one local community that all seeds are most likely to be in or finding a single community for each of the seeds. However, a seed member usually belongs to multiple local overlapping communities. In this work, we present a novel method of detecting multiple local communities to which a single seed member belongs. The proposed method consists of three key steps: (1) local sampling with Personalized PageRank (PPR); (2) using the sparseness generated by a sparse nonnegative matrix factorization (SNMF) to estimate the number of communities in the sampled subgraph; (3) using SNMF soft community membership vectors to assign nodes to communities. The proposed method shows favorable accuracy performance and a good conductance when compared to state-of-the-art community detection methods by experiments using a combination of artificial and real-world networks.

中文翻译:

用于多局部社区检测的稀疏非负矩阵分解

本地社区检测包括找到一组与种子密切相关的节点,即一小组感兴趣的节点。这样的节点组是密集连接的,或者与它们与网络中其他集群的连接相比,内部连接的可能性更高。现有的本地社区检测方法侧重于找到所有种子最有可能所在的一个本地社区,或者为每个种子找到一个社区。但是,种子成员通常属于多个本地重叠社区。在这项工作中,我们提出了一种检测单个种子成员所属的多个本地社区的新方法。所提出的方法包括三个关键步骤:(1) 使用个性化 PageRank (PPR) 进行局部采样;(2)利用稀疏非负矩阵分解(SNMF)产生的稀疏性来估计采样子图中的社区数量;(3)使用SNMF软社区成员向量为社区分配节点。通过结合使用人工和现实世界网络的实验,与最先进的社区检测方法相比,所提出的方法显示出良好的准确度性能和良好的电导率。
更新日期:2020-05-11
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