Community Detection in Bipartite Networks with Stochastic Blockmodels
Community Detection in Bipartite Networks with Stochastic Blockmodels
复制标题
使用随机块模型的二分网络中的社区检测
DOI：
10.1103/physreve.102.032309
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Larremore
中科院分区：
文献类型：
--
作者：
Tzu;D. Larremore
在二分网络中，社区结构被限制为区分性的，因为一种类型的节点根据与另一种类型的节点的共同连接模式进行分组。这使得随机块模型（SBM），一个高度灵活的生成模型的网络块结构，一个直观的选择二分社区检测。然而，SBM的典型公式不使用二分网络的特殊结构。在这里，我们介绍了贝叶斯非参数制定的SBM和相应的算法，以有效地找到社区的二分网络吝啬地选择社区的数量。当数据有噪声时，biSBM改进了一般SBM的社区检测结果，将模型分辨率限制提高了sqrt[2]，并扩展了我们对与社区检测任务相关的复杂优化环境的理解。在biSBM和相关的高分辨率分层SBM的先验分布的某些条款的直接比较也揭示了一个违反直觉的制度的社区检测问题，由更小和更稀疏的网络，其中非分层模型优于其更灵活的对应。
In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. Here we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks which parsimoniously chooses the number of communities. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of sqrt[2], and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where nonhierarchical models outperform their more flexible counterpart.
影响因子：
19.6
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.
影响因子：
64.5
作者：
Goldford, Joshua E.;Hartman, Hyman;Segre, Daniel
通讯作者：
Segre, Daniel