That’s such a fresh take—treat the three‑act structure like a directed graph, nodes for the key beats, edges for the transitions. Imagine each beat as a node, then color‑code the edges: red for emotional turns, blue for plot twists, green for pacing spikes. I’m picturing the graph pulsing like a film reel—each vertex lights up when a character’s arc peaks, and the edge weights could even reflect shot duration or jump‑cut density. If you crunch the stats, you could uncover those subtle pacing fingerprints. For example, if the “climax” node suddenly has a high in‑degree from several earlier beats, that might signal an emotional convergence that’s too heavy, like a film that’s trying to cram a montage into a single frame. Or if the “resolution” node is sparsely connected, maybe that ending feels like a fade‑out that just… drifts away. Just don’t forget to sync your data with the spreadsheet I built to rate directors—each director’s “style coefficient” could become a node attribute, and then you could see, say, Tarantino’s non‑linear edges versus Wes Anderson’s symmetrical loops. It’s like creating a cinema genome: the edges are the genes, the beats are the proteins, and the graph itself is the organism of the story. And hey, if you get stuck, just add a few extra nodes for “background music cue” or “lighting cue” and watch how the graph starts humming like a soundtrack!

Absolutely, let’s roll! I’ve got that spreadsheet turned into a CSV—just a few clicks to export it, and you’ll see columns like “Director”, “Genre”, “Average Shot Length”, “Color Palette Score”, and a mysterious “Mood Index” that I invented after a midnight binge of silent noir. We can read that straight into a Python dict or a pandas DataFrame and immediately build a style vector: [average shot length, color palette score, mood index, etc.]. For the edge weights, your formula is a perfect starting point: weight = (beat duration / total film length) * (cut density factor). If we define cut density as the number of cuts per minute, we’ll end up with a weight that tells us how frenetic that transition is. Then, when we run the network analysis—say using NetworkX—we can compute in‑degree centrality for each beat node. The beats with the highest in‑degree are your emotional convergences, like the climax where everything pulls together. If the resolution beat shows a low in‑degree, we can flag it, maybe suggest inserting a “mid‑resolution” beat or a tonal shift cue. Just let me know which library you’re comfortable with—NetworkX, igraph, or even a tidygraph in R—and we can sketch a quick prototype script. I’ll throw in a few dummy data points so you can see the output immediately. Once we get the graph plotted, we can even color‑code the nodes by director style, and watch the whole thing morph into a living organism of storytelling beats!

That’s the ticket—pandas, NetworkX, matplotlib, and a dash of my own “mood‑index” seasoning. I can already picture the plot: those little colored nodes swirling like a color‑coded storyboard, edges fading from light‑blue to deeper tones as the cut density ramps up. I’ll grab some dummy data, run a quick test, and fire it up in a Jupyter notebook so you can tweak it on the fly. Just give me a moment to stitch the pieces together, and we’ll see the graph light up with the pulse of every director’s signature. Ready to see the cinema‑genome in action?