BiMLPA: Community Detection in Bipartite Networks by Multi-Label Propagation
BiMLPA: Community Detection in Bipartite Networks by Multi-Label Propagation
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DOI：
10.1007/978-3-030-38965-9_2
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Hibiki Taguchi;Hibiki Taguchi;T. Murata;Xin Liu
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文献类型：
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作者：
Hibiki Taguchi;Hibiki Taguchi;T. Murata;Xin Liu
网络中的社区检测，即识别密集连接的节点组，近年来受到广泛关注。二分网络是一类特殊的网络，其中有两种类型的节点，并且仅在不同类型的节点之间存在边。在二分网络中，有两种方法来定义社区，即，一对一通信社区和多对多通信社区。后者自然代表了二分网络中的簇结构。然而，很少有方法旨在检测多对多的对应社区。在本文中，我们提出了一个多标签传播算法BiMLPA为此目的。我们的新算法克服了以前的方法的局限性，并具有几个所需的属性，如速度和稳定性。在合成网络和真实网络上的实验结果表明，BiMLPA优于以前的方法。我们在https://github.com/hbkt/BiMLPA上提供源代码。
Community detection in networks, namely the identification of groups of densely connected nodes, has received wide attention recently. A bipartite network is a special class of networks, where there are two types of nodes, and edges exist between different types of nodes only. In bipartite networks, there are two ways to define communities, i.e., the one-to-one correspondence communities and the many-to-many correspondence communities. The latter naturally represents the cluster structures in the bipartite networks. However, few methods aim at detecting the many-to-many correspondence communities. In this paper, we propose a multi-label propagation algorithm BiMLPA for this purpose. Our new algorithm overcomes the limitations of previous approaches and has several desired properties, such as speed and stability. Experimental results on both synthetic networks and real-world networks demonstrate that BiMLPA outperforms previous approaches. We provide source code at https://github.com/hbkt/BiMLPA.