An inherent limitation of many popular community detection methods, such as the walktrap and spin glass algorithms, is that they do not allow vertices to have membership in more than one community. Clique percolation remedies this limitation by allowing overlapping communities but does not necessarily produce solutions in accordance with the standard definition of ‘community’ (i.e., a dense subgraph of the network), often fails to assign all vertices to at least one community and presents formidable model selection challenges. In this paper, we propose a set-covering approach to overlapping community detection that enables overlapping communities to be assembled from maximal cliques, or from candidate communities formed from k-1 adjacent cliques. The promise of this new approach is demonstrated via comparison to clique percolation in a simulation experiment, as well as through application to an empirical psychological network.

许多流行的社区检测方法（例如 walktrap 和 spin glass 算法）的一个固有限制是它们不允许顶点拥有多个社区的成员资格。集团渗透通过允许重叠的社区来弥补这一限制，但不一定会根据“社区”的标准定义（即网络的密集子图）产生解决方案，通常无法将所有顶点分配给至少一个社区，并且呈现出强大的性能模型选择的挑战。在本文中，我们提出了一种用于重叠社区检测的集合覆盖方法，该方法使得重叠社区能够从最大派系或从k形成的候选社区中组装而成-1 个相邻派系。这种新方法的前景通过与模拟实验中的派系渗透的比较以及在经验心理网络中的应用得到了证明。