23 papers · ranked by Valyu relevance
Manuel Lafond, Francis Sarrazin
We study the parameterized complexity of the Cograph Deletion problem, which asks whether one can delete at most k edges from a graph to make it P4-free. This is a well-known graph modification problem with applications in computation biology and social network analysis. All current parameterized algorithms use a…
P. Sievers, George Skretas, Georg Tennigkeit
Network redesign problems ask to modify the edges of a given graph to satisfy some properties. In temporal graphs, where edges are only active at certain times, we are sometimes only allowed to modify when the edges are going to be active. In practice, we might not even be able to perform all of the necessary…
Haoxuan Xie, Junfeng Liu, Siqiang Luo, Kai Wang
Dynamic graphs model many real-world applications, and as their sizes grow, efficiently storing and updating them becomes critical. We present RadixGraph, a fast and memory-efficient data structure for dynamic graph storage. RadixGraph features a carefully designed radix-tree-based vertex index that strikes an optimal…
Seth Sullivant
The displayed tree phylogenetic network model is shown to sit as a natural submodel of the graphical model associated to a directed acyclic graph (DAG). This representation allows us to derive a number of results about the displayed tree model. In particular, the concept of a local modification to a DAG model is…
Rohit Lohani, Ravi Suthar, Krishnendra Shekhawat
This paper introduces a graph-theoretic framework for constructing floor plans that accommodate non-rectangular modules, with a focus on L-shaped and T -shaped geometries. In contrast to conventional methods that primarily address outer boundaries, the proposed approach incorporates structural constraints that arise…
Bansari. J. Rayjada, Jekil A. Gadhiya, Mahadityasinh Sarvaiya
Discrete Mathematics and Theoretical Computer Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vol. 25:3 special issue for main purpose #1 (2022)
Jie Huang, Qiaoyan Sun, Na Zhang, Meizhu Zheng + 1 more
Hypergraph Neural Networks (HGNNs) have become an important tool for processing complex structured data due to their ability to model higher-order associative relationships. However, the inherent adversarial vulnerabilities of HGNNs may raise serious security risks. The associated risks are far more pronounced in…
Authors not listed
We provide an overview of core molSimplify functionality and recent updates that enhance its capabilities for automated molecular and materials modeling. We describe the mol3D and atom3D classes, which store atomic and bonding information for a wide range of functions, including reading, modifying, and characterizing…
Yuning Guo, Qiang Wu, Linyuan Lü, Stanisław Drożdż
The tasks of community detection in complex networks have garnered increasing attention from researchers. Concurrently, with the emergence of graph neural networks (GNNs), these models have rapidly become the mainstream approach for solving this task. However, GNNs frequently encounter the Laplacian oversmoothing…
Zhanyong Jiao, Jiarui Fan, Ruochen Zhang, Dinghan Duan + 1 more
Structural balance in fully signed networks, integrating both individual attributes and relationships, represents a critical challenge in social computing; however, its dynamic transformation remains underexplored. This study extends structural balance theory by incorporating node attributes and formulating a…
Chao Lei, Yuzhi Xiao, Sheng Jin, Tao Huang + 3 more
Community detection is a crucial technique for uncovering latent network structures, analyzing group behaviors, and understanding information dissemination pathways. Existing methods predominantly rely on static graph structural features, while neglecting the intrinsic dynamic patterns of information diffusion and…
Bo Peng, Huan Xu, Xiangjiu Che
Introduction Graph data representation is widely applicable in numerous real-world scenarios, and recent advances in graph neural networks (GNNs) have enabled effective modeling of complex associations in graph-structured data. However, GNNs are often constrained by the over-smoothing problem, which reduces their…
Hongyan Gao, Xue Zhou, Lei Bai, Haifeng Yang + 1 more
m1A modification, as a pivotal RNA epigenetic modification, plays a central regulatory role in the pathogenesis and progression of complex human diseases, including cancer. Exploring the potential associations between m1As and diseases are an important approach to revealing the molecular mechanism of disease onset.…
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
Samuel Anyaso-Samuel, Shilan Li, Giovanny Herrera Ossa, Emily Vogtmann + 15 more
Biological traits such as genes, metabolites, and microbial taxa interact within complex networks, yet how genomic factors shape these interactions remains poorly understood. Here, we introduce GFBioNet, a computationally efficient method for identifying factors that modulate direct associations between biological…
Knut Vanderbush, Melanie Weber
Node coloring is the task of assigning colors to the nodes of a graph such that no two adjacent nodes have the same color, while using as few colors as possible. Node coloring is the most widely studied instance of graph coloring and of central importance in structural graph theory; major results include the Four Color…
Heming Zhang, Yifei Lu, Kaiwen Fang, Zixi Xu + 6 more
Medical records and omics data are rapidly becoming standard in healthcare settings, which characterize the whole-person from dysfunctional molecules to phenotypes, and thus offer potential for precise disease diagnosis and target discovery. Whereas, it remains an open problem to systematically integrate and interprete…
Authors not listed
Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative…
David Burke James Mahoney, Finlay Maguire
Environmental surveillance using metagenomic sequencing offers a powerful way to track emerging and mobile antimicrobial resistance (AMR) genes and inform public health mitigation strategies. Read-based analysis tools can sensitively detect AMR genes in metagenomes but provide little information about the surrounding…
Anna Lisiecka, Agnieszka Kowalewska, Norbert Dojer
Pangenome graphs conveniently represent genetic variation within a population. Several types of such graphs have been proposed, with varying properties and potential applications. Among them, variation graphs (VGs) seem best suited to replace reference genomes in sequencing data processing, while whole genome…
Authors not listed
Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. Quantum chemical methods for computing TSs are…
Authors not listed
Site-specific modifications of native-sequence proteins are technologies that underpin progresses in chemical biology, diagnostics and next-generation biotherapeutics. However, the pursuit of site-specificity has often come at the expense of scalability and usability, ultimately limiting translational potential of a…
Authors not listed
Computational toxicology plays a pivotal role in modern drug discovery and environmental risk assessment; however, the reliability of predictive models on unseen chemical scaffolds remains a critical bottleneck. Deep learning architectures, despite their prevalence, are susceptible to ’silent failures’—yielding…