How to pick diverse, influential network nodes?
Proportional Selection in Networks
This paper tackles the challenge of selecting a representative subset of nodes from a network, balancing influence (like PageRank or Katz centrality) with proportional representation of different groups within the network. Two approaches are proposed: 1) adapting voting rules from social choice theory to network selection, and 2) an "absorbing" method where selected nodes no longer propagate influence, encouraging diverse selections. The methods are analyzed theoretically and experimentally.
For LLM-based multi-agent systems, this research offers new ways to select influential agents while ensuring diverse perspectives are represented. The proposed methods could help mitigate bias and improve fairness in multi-agent collaborations, especially when agents influence each other through communication or delegation. The concept of balancing individual agent influence with proportional representation is directly applicable to coordinating groups of LLMs and enhancing the fairness of group decisions.