Author

Zeru Zhu

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#machine learning Preprint Oct 2026

Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion

A density-dependent approach is proposed that decomposes the design task into two stages: a high-quality, low-cost structural backbone construction and a reinforcement learning completion stage, and these results support a structural-family-first strategy for scalable network design.

Ze-Ru Zhu, Marco Gamarra, Ji Liu · 0 citations

Preprint Aug 2026

Maximizing Algebraic Connectivity with $2(n-2)$ Edges: The Large Vertex Number Case

Kolokolnikov conjectured that, among all simple graphs on $n$ vertices with exactly $2(n-2)$ edges, the complete bipartite graph maximizes algebraic connectivity. This paper proves the conjecture. The underlying Lean~4 formalization was generated with MerLean and checked by the Lean kernel.

Zeru Zhu, Jinzheng Li, Yuan-Jie Ren et al. · 2 citations · ⚡1