Efficiently inferring community structure in bipartite networks.
Efficiently inferring community structure in bipartite networks.
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DOI：
10.1103/physreve.90.012805
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发表时间：
2014-07
期刊：
影响因子：
--
通讯作者：
Jacobs AZ
中科院分区：
文献类型：
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作者：
Larremore DB;Clauset A;Jacobs AZ
二分网络是一种常见的网络数据类型，其中有两种类型的顶点，并且只有不同类型的顶点可以连接。虽然二分网络表现出像他们的unipartite同行的社区结构，现有的方法来二分社区检测有缺点，包括隐式参数选择，通过单模式投影的信息丢失，以及缺乏可解释性。在这里，我们解决了社区检测问题的二部网络制定一个二部随机块模型，其中明确包括顶点类型的信息，并可以平凡地扩展到k-部网络。这种二分随机块模型产生了一个无投影和统计原则的社区检测方法，使明确的假设和参数选择，并产生可解释的结果。我们证明了这个模型的能力，有效地和准确地找到社区结构在合成的二分网络与已知的结构和在现实世界中的二分网络与未知的结构，我们在实际情况下，其性能的特点。
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model’s ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.