# A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns

arXiv: https://arxiv.org/abs/2411.01143 · HTML: https://mlsanity.com/paper/2411.01143/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025)
- [Modeling Earth-Scale Human-Like Societies with One Billion Agents](https://mlsanity.com/paper/2506.12078/) (2025)
- [DynamiX: Large-Scale Dynamic Social Network Simulator](https://mlsanity.com/paper/2507.19929/) (2025)

## Abstract (arXiv)

In the digital world, influencers are pivotal as opinion leaders, shaping the views and choices of their influencees. Modern advertising often follows this trend, where marketers choose appropriate influencers for product endorsements, based on thorough market analysis. Previous studies on influencer selection have typically relied on numerical representations of individual opinions and interactions, a method that simplifies the intricacies of social dynamics. In this work, we first introduce a Time-aware Influencer Simulator (TIS), helping promoters identify and select the right influencers to market their products, based on LLM simulation. To validate our approach, we conduct experiments on the public advertising campaign dataset SAGraph which encompasses social relationships, posts, and user interactions. The results show that our method outperforms traditional numerical feature-based approaches and methods using limited LLM agents. Our research shows that simulating user timelines and content lifecycles over time simplifies scaling, allowing for large-scale agent simulations in social networks. Additionally, LLM-based agents for social recommendations and advertising offer substantial benefits for decision-making in promotional campaigns.