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Yi-Xuan Lee

Publications and source records attributed to Yi-Xuan Lee.

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Local Certification of Vertex and Edge Connectivity

Local certification is a framework for verifying global graph properties using only local information. In this model, a prover assigns short labels, called certificates, to the vertices of a graph. Each vertex then exchanges certificates with its neighbors and performs a purely local check to determine whether the graph satisfies the desired property. This line of research has led to efficient certification schemes for a broad range of graph classes, including minor-closed families, topological graph classes, and graphs defined by forbidden subgraphs. In this paper, we study the local certification of graph connectivity. Prior work by Bousquet, Feuilloley, and Pierron (JPDC 2024) showed that $2$-vertex-connectivity, $2$-edge-connectivity, and $3$-vertex-connectivity admit $O(\log n)$-bit certificates, leveraging structural characterizations such as ear decompositions. We go substantially beyond these cases and investigate general $k$-vertex-connectivity and $k$-edge-connectivity. We develop new approaches that exploit connections between connectivity and combinatorial structures, including branchings, Eulerian subgraphs, and independent spanning trees. For $k$-edge-connectivity, we obtain an $O_k(\log n)$-bit certification scheme and prove a matching $Ω_k(\log n)$ lower bound for every $k\ge 3$. The lower bound also applies to $k$-vertex-connectivity. For $k$-vertex-connectivity, we obtain $\tilde{O}_k(\sqrt{n})$-bit certificates for every $k$ under a conjecture of Itai and Zehavi. We further show that, for $k=2$, the logarithmic barrier can be broken on sparse graph classes: $2$-edge-connectivity admits constant-size certificates in bounded-expansion graphs, and $2$-vertex-connectivity admits constant-size certificates in bounded-degree graphs. In contrast, for $2$-vertex-connectivity in general graphs, we prove an $Ω(\log(\log^\ast n))$-bit lower bound.

cs.DC

On the Simulation Power of Surface Chemical Reaction Networks

The Chemical Reaction Network (CRN) is a well-studied model that describes the interaction of molecules in well-mixed solutions. In 2014, Qian and Winfree [22] proposed the abstract surface chemical reaction network model (sCRN), which takes the advantage of spatial separation by placing molecules on a structured surface, limiting the interaction between molecules. In this model, molecules can only react with their immediate neighbors. Many follow up works study the computational and pattern-construction power of sCRNs. In this work, our goal is to describe the power of sCRN by relating the model to other well-studied models in distributed computation. In this work, our main result is to show that, given the same initial configuration, sCRN, affinity strengthening tile automata, cellular automata, and amoebot can all simulate each other (up to unavoidable rotation and reflection of the pattern). One of our techniques is a coloring on-the-fly, which allows all molecules in sCRN to have a global orientation.

cs.CC