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arXiv Machine Learning By Zeru Zhu, Marco Gamarra, Ji Liu

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

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The paper tackles the problem of designing graph topologies that maximize algebraic connectivity while keeping the number of vertices and edges fixed. It introduces a two‑stage, density‑dependent approach: first constructing a low‑cost structural backbone—using complete multipartite graphs in dense settings and Cayley graphs in low‑to‑intermediate settings—then refining the remaining edges with a reinforcement learning completion stage. Numerical experiments demonstrate that this method achieves high algebraic connectivity with good runtime scalability, outperforming existing techniques and supporting a structural‑family‑first strategy for scalable network design.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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