惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
小众软件
小众软件
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 司徒正美
博客园 - 聂微东
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
美团技术团队
宝玉的分享
宝玉的分享
量子位
V
Visual Studio Blog
罗磊的独立博客
Vercel News
Vercel News
B
Blog
J
Java Code Geeks
S
SegmentFault 最新的问题
Recent Announcements
Recent Announcements
有赞技术团队
有赞技术团队
P
Proofpoint News Feed
GbyAI
GbyAI
G
Google Developers Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Overlapping Biclustering
Matthias Bentert, Pål Grønås Drange, Erlend Haugen · 2025-05-08 · via cs.DS updates on arXiv.org

We study the problem of transforming bipartite graphs into bicluster graphs. Abu-Khzam, Isenmann, and Merchad [IWOCA '25] introduced two variants of this problem. In both problems, the goal is to transform a bipartite graph into a bicluster graph with at most $k$ operations, where the allowed operations are inserting an edge, deleting an edge, and splitting a vertex. Splitting a vertex $v$ refers to replacing $v$ by two new vertices whose combined neighborhood equals the neighborhood of $v$. The latter models overlapping clusters, that is, vertices belonging to multiple clusters, and is motivated by several real-world applications. The versions differ in that one variant allows splitting any vertex, while the second variant only allows vertex splits on one side of the bipartition. Regarding computational complexity, they showed APX-hardness for both variants and a polynomial kernel (with $O(k^5)$ vertices) for the one-sided variant. They asked as open problems whether the polynomial kernel can be improved and whether it can also be extended for the other variant. We answer both questions in the affirmative and give kernels with $O(k^2)$ vertices for both variants. We also show that both problems can be solved in $O(k^{11k} + n + m)$ time, where $n$ and $m$ denote the number of vertices and edges in the input graph, respectively.