communitieseach

Communitieseach is a term used in social network analysis and data science to describe an approach that treats each detected community as a distinct unit of analysis. Rather than reporting network statistics at the level of the entire graph, researchers employing communitieseach examine intra-community structure, function, and dynamics individually while also summarizing cross-community links separately.

Origin and usage

The term communitieseach is a neologism that has appeared in discussions about modularity and per-community reporting. It is not tied to a single organization and is not universally adopted; rather, it describes a methodological stance that can be used within broader network analysis workflows. In practice, it is most often invoked when researchers want to compare communities directly or to tailor metrics to the peculiarities of each community rather than applying a single set of measures to the whole network.

Methodology

The approach typically begins with detecting communities using standard algorithms (for example, modularity-based methods or flow-based algorithms). For each community, analysts compute metrics such as size, internal density, average degree, clustering, internal centrality of key members, and the composition of roles. They also assess cross-community connections by examining boundary edges and inter-community flows, producing per-community profiles and summaries. Results are often visualized as a collection of community-level dashboards or profiles that can be compared side-by-side.

Applications and limitations

Communitieseach is used in settings such as social media analysis, collaboration networks, organizational studies, and urban planning where understanding the distinct characteristics of individual communities is valuable. Limitations include dependence on the initial community detection result, potential overspecification if many metrics are computed per community, and challenges when communities overlap or change over time. Related topics include community detection and modularity.