How can I efficiently sparsify graphs for LLM agents?
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning
This paper proposes a method for simplifying graph representations in graph-based machine learning by leveraging concepts from network control theory, specifically "zero-forcing." This simplification identifies a critical "backbone" structure (a tree) that preserves essential properties of the original graph, leading to more efficient learning without significant performance loss. This is analogous to finding a "winning ticket" (a sparse subnetwork within a larger graph) capable of achieving comparable performance.
The key points related to LLM-based multi-agent systems are the use of graphs to represent agent interactions and the focus on finding minimal structures that preserve essential properties. This could be applied to multi-agent communication, where the backbone could represent a simplified, efficient communication channel preserving information flow crucial for coordination and task completion, analogous to how LLMs might communicate with each other in a distributed system. The focus on controllability also hints at possibilities for controlling or steering the behavior of multi-agent systems based on a simplified understanding of their interaction network.