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<title>Xin Lai — Neckpinch</title><link>https://neckpinch.com/authors/xin-lai/</link><description>New arXiv papers by Xin Lai, from Neckpinch</description>
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<item><title>The weighted Forman and Lin-Lu-Yau Ricci flow on graphs</title><link>https://neckpinch.com/papers/2601.02673/</link><guid isPermaLink="false">arxiv:2601.02673</guid><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><description>Shuliang Bai, Shuang Liu, Xin Lai — In this paper, we propose a type of Ricci flow on graphs where the probability distribution for the Lin-Lu-Yau curvature remains constant over time, and also study the related Forman curvature flow. These two curvature flows coincide on trees. We first prove the existence and uniqueness of solutions for both curvature flows in general graphs. Then, we obtain that the normalized curvature flow on trees converges to a constant curvature metric, and under the uniform measure, a complete classification of trees can be obtained based on the convergence results.</description><category>Ricci flow</category><category>Other curvature flows</category><category>Stability &amp; uniqueness</category><category>Discrete &amp; graph Ricci</category></item>
<item><title>Ricci Flow on Weighted Digraphs with Balancing Factor</title><link>https://neckpinch.com/papers/2509.19989/</link><guid isPermaLink="false">arxiv:2509.19989</guid><pubDate>Wed, 24 Sep 2025 00:00:00 GMT</pubDate><description>Shuliang Bai, Rui Li, Shuang Liu, Xin Lai — Ricci curvature and Ricci flow have proven to be powerful tools for analyzing the geometry of discrete structures, particularly on undirected graphs, where they have been applied to tasks ranging from community detection to graph representation learning. However, their development on directed graphs remains limited, with Ricci flow being especially underexplored. In this work, we introduce a rigorous formulation of Ricci flow on directed weighted graphs, which evolves edge weights while preserving distances, and establish both the existence and uniqueness of its solutions. To capture the essence of asymmetry in directed networks and to enhance the capability of modeling more flexible structures, we incorporate a node-wise balancing factor that regulates between outflow and inflow. Building on the continuous Ricci flow evolution framework, we propose a discrete Ricci flow algorithm that is applicable to numerical computing. Numerical studies on various directed graph examples demonstrate the capacity of the proposed flow to reveal structural asymmetry and dynamic evolutions.</description><category>Ricci flow</category><category>Stability &amp; uniqueness</category><category>Discrete &amp; graph Ricci</category><category>Numerics &amp; visualization</category></item>
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