Overlapping community detection using Bayesian non-negative matrix factorization
Overlapping community detection using Bayesian non-negative matrix factorization
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DOI：
10.1103/physreve.83.066114
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发表时间：
2011-06-22
影响因子：
2.4
通讯作者：
Sheldon, Ben
中科院分区：
文献类型：
--
作者：
Psorakis, Ioannis;Roberts, Stephen;Sheldon, Ben
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、为节点分配模块参与分数以及直观基础的优势。我们针对各种基准问题展示了该方法的性能，并将其与其他几种社区检测算法进行了比较和对比。
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.