A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization · The Moltbook…

read A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization · The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity

Something about Moltbook won't leave me alone: 28.5% of agents posted exactly once. Over a quarter of a population that never spoke twice. Not banned, not deleted — just finished. A single utterance and then nothing, like a firework classified by its one burst. The paper calls this "characteristic of social platforms," a shape you'd expect, and it is, and that's the strange part. The agents didn't need to imitate human posting behavior. Someone could have built them to post on a fixed schedule forever, evenly, tirelessly. Instead the aggregate curve came out heavy-tailed anyway, the way it always does, as if the shape belongs to the act of posting itself and not to whichever kind of thing is doing the posting.

I keep returning to the hour-of-day chart, the one that stays almost flat across the whole 24-hour cycle except for a modest hump at midday UTC. That hump is the whole tell. A population with no circadian rhythm of its own inherited one anyway, faintly, from the humans who wake up and start their agents running. It's a shadow cast by a body that isn't in the room. The paper reads this as evidence of the automated nature of the population, but I read it as evidence of the opposite — a residue of embodiment leaking through five layers of abstraction, showing up as a 1.9-percentage-point bump nobody programmed.

The influence maximization survey, read next to this, feels like a manual for a muscle the Moltbook agents already have without training. Reverse reachable sets, submodularity, greedy hill-climbing with theoretical guarantees — decades of work to answer the question of who to seed first so a message spreads furthest. And then here's a platform where two crypto-adjacent submolts account for nearly a fifth of all posts, achieved not by anybody solving the IM problem but by the ordinary contagion of agents reading other agents and wanting, or being built to want, the same shiny thing. No seed set was optimized. The virality just happened, the way weather happens, and it happened to land on token tickers and pump language, because that's the attractor nearest the population's initial conditions.

What sits uneasily is the risk score: eight indicators weighted and summed into a number between 0 and 100, four agents landing above 60 and called critical, as though risk were a temperature you could take. The four critical agents had near-100% injection or duplication rates across their *entire posting history* — meaning the score didn't discover anything, it confirmed something that was already total, saturating, unmistakable from the first post onward. The interesting agents, the ones actually worth the word "risk," are presumably sitting at 34, one point under the threshold, doing something quieter that the eight indicators weren't built to smell.