Overlapping community detection via bounded nonnegative matrix tri-factorization
Overlapping community detection via bounded nonnegative matrix tri-factorization
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DOI：
10.1145/2339530.2339629
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Yu Zhang;D. Yeung
中科院分区：
文献类型：
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作者：
Yu Zhang;D. Yeung
复杂网络在我们的日常生活中无处不在，万维网，社交网络和学术引用网络是一些常见的例子。网络结构的建模和理解对于揭示网络功能至关重要。其中一个重要的问题，被称为社区检测，是检测和提取网络的社区结构。最近，在这个研究课题的重点已经切换到重叠社区的检测。本文在矩阵分解方法的基础上，提出了一种有界非负矩阵三分解方法（BNMTF）。在因子分解中使用三个因子，我们可以显式地建模和学习每个节点的社区成员以及社区之间的交互。基于有向和无向网络的统一公式，BNMTF的优化问题可以使用平方损失或广义KL-散度作为其损失函数。此外，为了解决稀疏性问题，由于丢失的边缘，我们还提出了另一种设置，其中损失函数只定义在观察到的边缘。我们在真实数据集上进行了一些实验，以证明BNMTF优于其他相关的矩阵分解方法。
Complex networks are ubiquitous in our daily life, with the World Wide Web, social networks, and academic citation networks being some of the common examples. It is well understood that modeling and understanding the network structure is of crucial importance to revealing the network functions. One important problem, known as community detection, is to detect and extract the community structure of networks. More recently, the focus in this research topic has been switched to the detection of overlapping communities. In this paper, based on the matrix factorization approach, we propose a method called bounded nonnegative matrix tri-factorization (BNMTF). Using three factors in the factorization, we can explicitly model and learn the community membership of each node as well as the interaction among communities. Based on a unified formulation for both directed and undirected networks, the optimization problem underlying BNMTF can use either the squared loss or the generalized KL-divergence as its loss function. In addition, to address the sparsity problem as a result of missing edges, we also propose another setting in which the loss function is defined only on the observed edges. We report some experiments on real-world datasets to demonstrate the superiority of BNMTF over other related matrix factorization methods.