Link community detection using generative model and nonnegative matrix factorization.
Link community detection using generative model and nonnegative matrix factorization.
复制标题
使用生成模型和非负矩阵分解进行链接社区检测
DOI：
10.1371/journal.pone.0086899
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Liu D
中科院分区：
文献类型：
--
作者：
He D;Jin D;Baquero C;Liu D
复杂网络中社区的发现是一个基本的数据分析问题，在各个领域都有应用。虽然现有的大多数方法都侧重于发现节点的社区，但最近的研究显示了链路社区发现在网络中的优势和用途。生成模型为网络中模块化结构的识别提供了一种很有前途的技术，但大多数生成模型主要关注节点社区而不是链接社区的检测。在这项工作中，我们提出了一个生成模型，该模型基于每个社区中每个节点在形成链接时的重要性来描述链接社区的结构。将其作为优化问题进行模型参数拟合，并采用非负矩阵分解法进行求解。然后，为了自动确定群落的数量，我们扩展了上述方法，引入了迭代双划分策略。该方法不仅能自行找到社区数量，而且效率高，更适合于处理大型、未探索的真实网络。我们在合成基准测试和现实世界网络（包括大型生物网络上的应用程序）上测试了这种方法，并将其与两种高度相关的方法进行了比较。结果表明，我们的方法在链路社区检测方面优于其他竞争方法。
Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. While most of the existing approaches have focused on discovering communities of nodes, recent studies have shown the advantages and uses of link community discovery in networks. Generative models provide a promising class of techniques for the identification of modular structures in networks, but most generative models mainly focus on the detection of node communities rather than link communities. In this work, we propose a generative model, which is based on the importance of each node when forming links in each community, to describe the structure of link communities. We proceed to fit the model parameters by taking it as an optimization problem, and solve it using nonnegative matrix factorization. Thereafter, in order to automatically determine the number of communities, we extend the above method by introducing a strategy of iterative bipartition. This extended method not only finds the number of communities all by itself, but also obtains high efficiency, and thus it is more suitable to deal with large and unexplored real networks. We test this approach on both synthetic benchmarks and real-world networks including an application on a large biological network, and compare it with two highly related methods. Results demonstrate the superior performance of our approach over competing methods for the detection of link communities.
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