论文标题
多个本地社区检测的稀疏非负矩阵分解
Sparse Nonnegative Matrix Factorization for Multiple Local Community Detection
论文作者
论文摘要
当地社区的检测包括找到与种子密切相关的一组节点，这是一小部分感兴趣的节点。与网络中的其他群集相比，此类节点密度连接或具有内部连接的可能性很高。现有的当地社区检测方法着眼于寻找一个最有可能进入的当地社区，或者为每个种子找到一个单一的社区。但是，种子成员通常属于多个本地重叠社区。在这项工作中，我们提出了一种新的方法，用于检测单个种子成员所属的多个本地社区。提出的方法包括三个关键步骤：（1）用个性化Pagerank（PPR）进行本地抽样； （2）使用稀疏的非负矩阵分解（SNMF）产生的稀疏度来估计采样子图中的社区数量； （3）使用SNMF软社区会员媒介向社区分配节点。与最先进的社区检测方法相比，通过使用人工和现实世界网络的结合进行了实验，该方法表现出有利的精度性能和良好的电导率。
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.
