Testing the Binary Rank with Polynomial Query Complexity
本文提出了一种具有多项式查询复杂度的自适应双边误差测试算法，用于检测0,1矩阵的二进制秩，并能进一步找到该矩阵的一个近似二进制分解。
0 citationsRead paper
Selected work
Representative Papers
When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media
This study addresses the lack of systematic understanding regarding the mechanisms driving the evolution of ideological polarization in long-term online political discourse. To overcome the limitations of cross-sectional or sentiment-focused analyses, this work proposes a temporally aligned, community-specific word embedding approach that integrates semantic distance metrics with large-scale Reddit discussion data. For the first time, this framework enables longitudinal modeling of semantic divergence between opposing political groups on key concepts. Empirical results demonstrate a significant intensification of ideological polarization at both conceptual and topical levels over the study period, confirming the method’s capacity to effectively capture dynamic polarization processes in large-scale textual corpora.
0 citationsRead paper
Fair Allocation under Conflict Constraints via Strong Colorability
This work addresses the problem of fairly allocating vertices of a graph among multiple agents with identical preferences under conflict constraints, where adjacent vertices cannot be assigned to the same agent. The authors introduce, for the first time, a hierarchical framework based on the strong chromatic number to unify the modeling of fair allocation under three fairness criteria: SD-EF1, EF1, and EF[1,1]. Leveraging techniques from strong graph coloring, they design a deterministic polynomial-time algorithm whose performance depends on the graph’s maximum degree Δ. They prove that for any graph with maximum degree Δ, a fair allocation satisfying all three criteria exists whenever the number of agents is at least 3Δ−1. Moreover, when the number of agents is at least (3+ε)Δ for any constant ε>0, such an allocation can be efficiently constructed.
0 citationsRead paper
Setwise Distinguishable Permutations
This work investigates the construction of maximum-sized families of set-distinguishable permutations—collections in which each permutation admits a subset whose image under that permutation is distinct from its image under any other permutation in the family. Through an explicit combinatorial construction, the authors achieve a family size of $2^{(2 - o(1))n}$, asymptotically approaching the theoretical upper bound. This construction is applied for the first time to analyze conditional kernelization lower bounds for graph coloring parameterized by the vertex-deletion distance to split graphs, yielding nearly tight complexity lower bounds that substantially improve upon the prior results of Jansen and Kratsch (2013).
0 citationsRead paper
Kernelization Bounds for Constrained Coloring
This work investigates the kernelization complexity of constraint satisfaction problems over finite domains with disequalities and permutation-invariant relations, parameterized by the number of variables, as well as the existence of polynomial kernels for the Uniform Rainbow Independent Set problem. By characterizing the largest arity OR relation definable via a given relation R and leveraging tools from parameterized complexity, kernelization techniques, logical definability, and the conditional assumption that NP ⊈ coNP/poly, the study establishes the first optimal lower bounds on kernel size for Uniform Rainbow Independent Set across all relevant parameterizations. It also resolves an open question regarding vertex-deletion distance to disjoint unions of cliques in graph coloring. The results yield nearly tight upper and lower bounds on the kernel complexity for several graph and hypergraph coloring problems, unifying and extending prior work by Jansen–Pieterse and Beukers et al.
0 citationsRead paper
Recent publications
Latest Papers
Testing the Binary Rank with Polynomial Query Complexity
本文提出了一种具有多项式查询复杂度的自适应双边误差测试算法，用于检测0,1矩阵的二进制秩，并能进一步找到该矩阵的一个近似二进制分解。
0 citationsRead paper
When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media
This study addresses the lack of systematic understanding regarding the mechanisms driving the evolution of ideological polarization in long-term online political discourse. To overcome the limitations of cross-sectional or sentiment-focused analyses, this work proposes a temporally aligned, community-specific word embedding approach that integrates semantic distance metrics with large-scale Reddit discussion data. For the first time, this framework enables longitudinal modeling of semantic divergence between opposing political groups on key concepts. Empirical results demonstrate a significant intensification of ideological polarization at both conceptual and topical levels over the study period, confirming the method’s capacity to effectively capture dynamic polarization processes in large-scale textual corpora.
0 citationsRead paper
Fair Allocation under Conflict Constraints via Strong Colorability
This work addresses the problem of fairly allocating vertices of a graph among multiple agents with identical preferences under conflict constraints, where adjacent vertices cannot be assigned to the same agent. The authors introduce, for the first time, a hierarchical framework based on the strong chromatic number to unify the modeling of fair allocation under three fairness criteria: SD-EF1, EF1, and EF[1,1]. Leveraging techniques from strong graph coloring, they design a deterministic polynomial-time algorithm whose performance depends on the graph’s maximum degree Δ. They prove that for any graph with maximum degree Δ, a fair allocation satisfying all three criteria exists whenever the number of agents is at least 3Δ−1. Moreover, when the number of agents is at least (3+ε)Δ for any constant ε>0, such an allocation can be efficiently constructed.
0 citationsRead paper
Setwise Distinguishable Permutations
This work investigates the construction of maximum-sized families of set-distinguishable permutations—collections in which each permutation admits a subset whose image under that permutation is distinct from its image under any other permutation in the family. Through an explicit combinatorial construction, the authors achieve a family size of $2^{(2 - o(1))n}$, asymptotically approaching the theoretical upper bound. This construction is applied for the first time to analyze conditional kernelization lower bounds for graph coloring parameterized by the vertex-deletion distance to split graphs, yielding nearly tight complexity lower bounds that substantially improve upon the prior results of Jansen and Kratsch (2013).
0 citationsRead paper
Kernelization Bounds for Constrained Coloring
This work investigates the kernelization complexity of constraint satisfaction problems over finite domains with disequalities and permutation-invariant relations, parameterized by the number of variables, as well as the existence of polynomial kernels for the Uniform Rainbow Independent Set problem. By characterizing the largest arity OR relation definable via a given relation R and leveraging tools from parameterized complexity, kernelization techniques, logical definability, and the conditional assumption that NP ⊈ coNP/poly, the study establishes the first optimal lower bounds on kernel size for Uniform Rainbow Independent Set across all relevant parameterizations. It also resolves an open question regarding vertex-deletion distance to disjoint unions of cliques in graph coloring. The results yield nearly tight upper and lower bounds on the kernel complexity for several graph and hypergraph coloring problems, unifying and extending prior work by Jansen–Pieterse and Beukers et al.
0 citationsRead paper