DEMON：一种局部优先的重叠社区发现方法
DEMON: a Local-First Discovery Method for Overlapping Communities
摘要：
复杂网络中的社区发现是一个有趣的问题，具有许多应用，特别是在社会和信息网络中的知识提取任务。然而，许多大型网络往往缺乏全球一级的特定社区组织。在这些情况下，传统的图划分算法无法让嵌入在模块化结构中的潜在知识显现出来，因为它们强加了一个自上而下的网络全局视图。我们在这里提出了一个简单的本地优先的方法来发现社区，能够揭开真正的复杂网络的模块化组织。这是通过民主地让每个节点投票给它在其有限的全局系统视图中看到的社区来实现的，即它的自我邻居，使用标签传播算法;最后，本地社区被合并成一个全局集合。我们对最先进的重叠和非重叠社区发现方法进行了测试，发现我们的新方法在获得社区的质量方面明显优于其他方法，通过使用提取的社区来预测有关几个真实世界网络节点的元数据进行评估。我们还展示了我们的方法是如何确定性的，完全增量的，并具有有限的时间复杂度，使它可以用于网络规模的真实网络。
英文摘要：
Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.
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。
