论文标题
双方混合会员分配模型。在重叠的两部分加权网络中的社区发现的新型模型
Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks
论文作者
论文摘要
近年来，已经对重叠的单分性非加权网络重叠的建模和估算成员资格进行了充分的研究。但是，据我们所知，没有一个更普遍的情况，即重叠的两部分加权网络。为了缩小这一差距，我们引入了一种新型模型，即双方混合成员分配（BIMMDF）模型。我们的模型允许邻接矩阵遵循任何分布，只要其预期具有与节点成员资格相关的块结构。特别是，BIMMDF可以建模重叠的两分符号网络，并且是许多以前模型的扩展，包括流行的混合成员随机随机BlcokModels。具有一致估计的理论保证的有效算法用于拟合BIMMDF。然后，我们获取用于不同分布的BIMMDF的分离条件。此外，我们还考虑缺少稀疏网络的边缘。 BIMMDF的优势在广泛的合成网络和八个现实世界网络中得到了证明。
Modeling and estimating mixed memberships for overlapping unipartite un-weighted networks has been well studied in recent years. However, to our knowledge, there is no model for a more general case, the overlapping bipartite weighted networks. To close this gap, we introduce a novel model, the Bipartite Mixed Membership Distribution-Free (BiMMDF) model. Our model allows an adjacency matrix to follow any distribution as long as its expectation has a block structure related to node membership. In particular, BiMMDF can model overlapping bipartite signed networks and it is an extension of many previous models, including the popular mixed membership stochastic blcokmodels. An efficient algorithm with a theoretical guarantee of consistent estimation is applied to fit BiMMDF. We then obtain the separation conditions of BiMMDF for different distributions. Furthermore, we also consider missing edges for sparse networks. The advantage of BiMMDF is demonstrated in extensive synthetic networks and eight real-world networks.
