Analysis of networks and in particular discovering communities within
networks has been a focus of recent work in several fields, with applications
ranging from citation and friendship networks to food webs and gene regulatory
networks. Most of the existing community detection methods focus on
partitioning the entire network into communities, with the expectation of many
ties within communities and few ties between. However, many networks contain
nodes that do not fit in with any of the communities, and forcing every node
into a community can distort results. Here we propose a new framework that
focuses on community extraction instead of partition, extracting one community
at a time. The main idea behind extraction is that the strength of a community
should not depend on ties between members of other communities, but only on
ties within that community and its ties to the outside world. We show that the
new extraction criterion performs well on simulated and real networks, and
establish asymptotic consistency of our method under the block model
assumption.

提出了一种基于社区提取而非划分的新框架，该提取方法一次只提取一个社区，以避免将每个节点强制归入社区而扭曲结果，其性能在模拟和真实网络上表现良好，并在模型假设下确立了我们方法的渐近一致性。