In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.
翻译:在本文中,我们提出了一个在网络中探测重叠社区的新的方法,网络中预先确定的数量是LPAM(围绕类动物的链条分割),图中的重叠社区是通过在相关线图中探测离散社区的方法获得的,该线图使用在一组节点上界定的距离函数,在类动物周围进行连接分割和分割。我们认为通勤距离和扩大通勤距离是距离函数。LPAM方法的性能是通过实际生活实例的计算实验以及合成网络基准来评估的。对于中小型网络来说,找到确切的解决方案,而对于大型网络来说,我们找到了使用LPAM方法的超光化版本的解决方案。