Relationships between characters are not just themes in a story but key elements that shape how plots unfold. This article presents a large-scale study of relational arcs, the trajectories of ties, such as kinship, romance, alliance and enmity as they rise and fall across the course of a novel. We build on the Artificial Relationships in Fiction dataset, which contains over 120,000 automatically annotated relationships from 96 novels published between 1850 and 1950. Our study makes four contributions. First, we show that relationship dynamics can be modeled as arcs that highlight recurring narrative patterns, such as conflicts peaking near the climax or romances resolving toward the end. Second, we use temporal normalization to compare books of very different lengths, allowing us to identify consistent trends across the corpus. Third, we demonstrate that genres and historical periods leave clear relational “fingerprints.” For instance, domestic fiction emphasizes family ties, while adventure stories highlight shifting alliances and adversaries. Finally, we cluster arcs into four common shapes (Rise, U-shape, Decline and Oscillating) that echo well-known narrative prototypes. By bringing narratology together with modern natural language processing, we argue that relationships provide a measurable grammar of plot. This approach offers new resources for literary analysis, new methods for computational modeling of narrative, and fresh evidence about how cultural storytelling patterns change over time.