How do spreader types impact information cascades in networks?
Heterogeneous Update Processes Shape Information Cascades in Social Networks
This paper explores how different "learning profiles" within a network affect information spread. It models two types of agents: "Simple Spreaders" who readily adopt and share information, and "Threshold-based Spreaders" who require multiple confirmations before adopting. The research reveals that strategically placing Threshold-based Spreaders in highly connected network positions can significantly curb information cascades, even when Simple Spreaders are abundant. This is especially true in networks with unevenly distributed connections (heterogeneous networks).
For LLM-based multi-agent systems, this research highlights the importance of agent behavior diversity and strategic placement for controlling information flow and potentially mitigating the spread of misinformation. The Simple Spreader acts like a bot or easily influenced user, while the Threshold-based Spreader can be seen as a cautious user or fact-checker. Manipulating the ratio and network positions of these agent types could be crucial for managing information cascades in multi-agent applications.