Psorakis Ioannis, Roberts Stephen, Ebden Mark, Sheldon Ben
Pattern Analysis and Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jun;83(6 Pt 2):066114. doi: 10.1103/PhysRevE.83.066114. Epub 2011 Jun 22.
Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment of node participation scores to modules, and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection.
识别网络中的重叠社区是一项具有挑战性的任务。在这项工作中，我们提出了一种用于社区检测的概率方法，该方法利用贝叶斯非负矩阵分解模型从网络中提取重叠模块。该方案具有软划分解决方案、为模块分配节点参与分数以及直观基础的优点。我们展示了该方法在各种基准问题上的性能，并将其与其他几种社区检测算法进行比较和对比。
