Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion
The research addresses maximizing algebraic connectivity for fixed networks by using a two-stage approach: first, building a structural backbone based on network density, and second, using reinforcement learning to add remaining edges. The method uses complete multipartite graphs for dense networks and Cayley graphs for sparser ones. This strategy yields high algebraic connectivity with good performance scaling.