What does this equation mean?

This is the one family among the five whose defining engineering trade-off is temporal rather than architectural: cost is shifted from query time to index time. Writing $C_{\text{index}}$ for the one-time cost of building the graph and its community summaries, and $c_{\text{marginal}}$ for the cost of answering a single query against the finished structure, the amortised cost of a query workload of size Q against one graph is $c_{\text{query}}(Q) = \frac{C_{\text{index}}}{Q} + c_{\text{marginal}}$. When Q is small — a corpus queried rarely, or one still being explored — $C_{\text{index}}$ dominates and the naive or hybrid architectures are cheaper by a wide margin, because they pay nothing until a query arrives. When Q is large against a stable corpus, the fixed term is driven down toward zero and the comparison turns on $c_{\text{marginal}}$ against the per-query cost of the alternatives. The direction of that comparison depends on the question being asked, not only on volume: Edge and colleagues report that GraphRAG produces substantially more comprehensive and diverse answers than a conventional RAG baseline specifically on query-focused summarisation over large corpora, the kind of question — “what are the main themes across this entire collection” — that a top- k chunk retriever structurally cannot answer well no matter how many times it is queried, because no fixed small set of chunks represents a corpus-wide theme [ 4 ] .