Can MDS improve LLM influence maximization in multilayer networks?
Applicability of the Minimal Dominating Set for Influence Maximisation in Multilayer Networks
February 24, 2025
https://arxiv.org/pdf/2502.15236This paper explores using the Minimal Dominating Set (MDS) to improve seed selection for influence maximization in multilayer networks. It examines how choosing initial influential agents based on MDS affects the spread of information or opinions under different network structures and spreading models.
Key points for LLM-based multi-agent systems:
- Improved Seed Selection: MDS can improve seed selection, leading to wider influence propagation, especially in networks with low activation thresholds and when influence needs to spread across all layers (like social circles). This could be useful for designing agents that effectively disseminate information.
- Multilayer Network Dynamics: The research highlights how influence spreads differently in multilayer networks compared to single-layer networks. This is relevant for LLM-based multi-agent systems operating in complex environments with diverse interactions, emphasizing that the relationships between agents are important.
- Control vs. Influence: The paper bridges concepts from control theory and influence maximization, showing how MDS, a concept from control theory, can be applied to the softer problem of influence. This suggests possibilities for using control-theoretic concepts to design and manage multi-agent systems.
- Limitations of MDS: MDS can be computationally costly and not always beneficial. Its effectiveness depends on network structure and spreading parameters. This emphasizes that care is needed in choosing whether to use MDS for seed selection.
- Relevance to Agent Design: MDS's tendency to select both central and peripheral nodes is important for agent communication strategies. In heterogeneous networks, agents may need to target both highly connected and less connected agents for optimal information spread.