This Python 3.x module bundles a set of useful code functions for humanistic inquiry of social networks. The module assumes that researchers have a set of network subgraphs created through community-detection, and they need to more quickly contextualize each community of importance in the corpus for further investigation. It was developed to help researchers create output to answer the following questions: What can these communities be named, and how can they grouped together? The aims of the module build on what Freelon, McIlwain, and Clark refer to as the “hubs” of each community: the top in-degree users from each community and sample of texts that mentions those users (2016, 2018). Yet, extending Freelon et al., this module can also accept each community’s top authors during a period. As a result of this extended hub, the module can also trace potential persistent authorship across communities and generate topic models for each sample to contextualize the hubs over time. Overall, the module helps researchers fulfill these contextualizing aims by producing output that answers the following specific questions: What community hubs persist, or are ephemeral, across periods in the corpus, and when? Of these community hubs, what are their topics over time?