Graphs Help Graphs: Multi-Agent Graph Socialized Learning
Jialu Li, Yu Wang, Pengfei Zhu, Wanyu Lin, Xinjie Yao, Qinghua Hu
Graphs in the real world are fragmented and dynamic, lacking collaboration akin to that observed in human societies. Existing paradigms present collaborative information collapse and forgetting, making collaborative relationships poorly autonomous and interaction information insufficient. Moreover, collaborative information is prone to loss when graph grows. Effective collaboration in heterogeneous dynamic graph environments becomes challenging. Inspired by social learning, this paper presents a Graph Socialized Learning (GSL) paradigm. We provide insights into graph socialization in GSL and boost performance of agents through effective collaboration. It is crucial to determine with whom, what, and when to share and accumulate information for effective GSL. Thus, we propose ''Graphs Help Graphs'' (GHG) method to solve these issues. Specifically, it uses graph-driven organizational structure to autonomously select interacting agents and manage interaction strength. We produce customized synthetic graphs as interaction medium based on demand of agents, then apply synthetic graphs to build prototypes in life cycle to help select optimal parameters. We demonstrate the effectiveness of GHG in heterogeneous dynamic graphs by extensive empirical study.