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

Apple Machine Learning Research
Apple Machine Learning Research
Jina AI
Jina AI
博客园_首页
WordPress大学
WordPress大学
罗磊的独立博客
小众软件
小众软件
Last Week in AI
Last Week in AI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Hugging Face - Blog
Hugging Face - Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
爱范儿
爱范儿
The Cloudflare Blog
GbyAI
GbyAI
C
Check Point Blog
腾讯CDC
MyScale Blog
MyScale Blog
有赞技术团队
有赞技术团队
博客园 - 聂微东
IT之家
IT之家
雷峰网
雷峰网
H
Help Net Security
博客园 - 叶小钗
美团技术团队
D
DataBreaches.Net

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
Bicluster Editing with Overlaps: A Vertex Splitting Approach
Faisal N. Abu-Khzam, Lucas Isenmann, Zeina Merchad · 2025-05-07 · via cs.DS updates on arXiv.org

The BiCluster Editing problem aims at editing a given bipartite graph into a disjoint union of bicliques via a minimum number of edge deletion or addition operations. As a graph-based model for data clustering, the problem aims at a partition of the input dataset, which cannot always obtain meaningful clusters when some data elements are expected to belong to more than one cluster each. To address this limitation, we introduce the Bicluster Editing with Vertex Splitting problem (BCEVS) which consists of finding a minimum sequence of edge editions and vertex splittings such that the resulting graph is a disjoint union of bicliques. The vertex splitting operation consists of replacing a vertex $v$ with two vertices whose union of neighborhoods is the neighborhood of $v$. We also introduce the problem of Bicluster Editing with One-Sided Vertex Splitting (BCEOVS) where we restrict the splitting operations to the only one set of the two sets forming the bipartition. We prove that the two problems are NP-complete even when restricted to bipartite planar graphs of maximum degree three. Moreover, assuming the {\sc Exponential Time Hypothesis} holds, there is no $2^{o(n)}n^{O(1)}$-time (resp. $2^{o(\sqrt{n})}n^{O(1)}$-time) algorithm for BCEVS and BCEOVS on bipartite (resp. planar) graphs with maximum degree three, where $n$ is the number of vertices of the graph. Furthermore we prove both problems are APX-hard and solvable in polynomial time on trees. On the other hand, we prove that BCEOVS is fixed-parameter tractable with respect to solution size by showing that it admits a polynomial size kernel.