Seeing Architecture: Communities, Hubs, and Surprises
A focused query can explain the neighborhood around a known symbol, but architecture is often discovered before the right symbol is known. Graphify addresses that problem by reading the graph at larger scales: communities reveal dense regions, centrality highlights highly connected entities, and surprising-connection analysis points toward boundaries that deserve investigation. This chapter explains how those lenses work together, and where a structural summary must stop short of claiming architectural intent.
The distinction is important because a graph can expose shape without explaining why the shape exists. A cluster may correspond to a subsystem, a temporary coupling, or an artifact of extraction. A high-degree node may organize a domain or merely be a file hub. A cross-community edge may reveal an important interface or a plausible but polluted inference. Graphify's analyses are useful when they change what the reader investigates next while keeping those interpretations tied to confidence and source evidence.
Communities reveal structure, not intent
Graphify uses Leiden-based community detection to group nodes by connectivity. In plain terms, nodes that share many relationships are more likely to end up in the same community than nodes connected only through a distant bridge. This gives the reader a first architectural orientation: instead of scanning every entity in one undifferentiated graph, they can ask which regions hold together and which links cross between them.
A community is therefore a statement about graph structure. It is not automatically a package boundary, a team boundary, or a formal subsystem named by the project's maintainers. The algorithm sees edges, not ownership, deployment topology, or organizational history. A codebase whose modules are tightly connected may produce one large community even if people think of it as several products. A shared utility or document hub can pull unrelated areas together. The useful question is not “which community is the real architecture?” but “what relationship pattern caused these nodes to be grouped, and is that pattern meaningful for the question I am asking?”
Graphify adds safeguards around that interpretation. Isolates are handled separately rather than being discarded by a clustering routine that expects connected input. Oversized communities can be split again, and low-cohesion groups can be reconsidered when a bridge node has joined otherwise weakly related regions. Cohesion measures how many of the possible within-community connections actually exist; it is a diagnostic of the grouping, not a score of software quality. A low-cohesion community says that the proposed region may contain too much internal distance for a reader to treat it as one coherent subsystem.
High-degree hubs need special care during clustering. Some nodes are useful as connectors but distort the partition if they are allowed to pull every neighbor into one region. Graphify can exclude high-degree utility hubs while partitioning and then reattach them to a majority-vote neighbor community.