社交网络的社区提取
Community extraction for social networks
摘要：
分析网络，特别是发现网络中的社区一直是最近几个领域的工作重点，应用范围从引用和友谊网络到食物网和基因调控网络。现有的大多数社区检测方法都集中在将整个网络划分为社区，期望社区内有许多联系，社区之间很少有联系。然而，许多网络包含不适合任何社区的节点，强制每个节点进入社区可能会扭曲结果。在这里，我们提出了一个新的框架，专注于社区提取，而不是分区，提取一个社区的时间。提取背后的主要思想是，一个社区的力量不应该取决于其他社区成员之间的联系，而应该只取决于该社区内部的联系以及与外部世界的联系。我们表明，新的提取标准进行模拟和真实的网络，并建立我们的方法下块模型假设的渐近一致性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
