Results 61 to 70 of about 10,068 (229)

Quantum versatility in PageRank

Physical Review Research
Quantum mechanics empowers the emergence of quantum advantages in various fields, including quantum algorithms. Quantum PageRank is a promising tool for a future quantum internet.
Wei-Wei Zhang, Zheping Wu, Hengyue Jia, Wei Zhao, Qingbing Ji, Wei Pan, Haobin Shi   +6 more
doaj   +1 more source

Token-Curated Registry with Citation Graph

Ledger, 2019
In this study, we aim to incorporate the expertise of anonymous curators into a token-curated registry (TCR), a decentralized recommender system for collecting a list of high-quality content.
Kensuke Ito, Hideyuki Tanaka
doaj   +1 more source

How can we enhance reputation in Blockchain consensus for Industry 4.0–A proposed approach by extending the PageRank algorithm

International Journal of Information Management Data Insights, 2022
This research presents a cost-effective approach toward an enhancement of Blockchain consensus targeting enhanced data security and privacy in the Industry 4.0 scenario by incorporating an enhanced reputation score at a cost-effective approach.
Arnab Mitra
doaj   +1 more source

Integrated Single‐Nucleus Multi‐Omics Atlases Reveal Lineage Plasticity and Regulatory Networks of Luminal Epithelial Cells During Mammary Gland Lactation and Involution

Advanced Science, EarlyView.
This study integrates single‐cell multi‐omics, spatial transcriptomics, and cross‐species comparative analyses to systematically characterize the cellular composition and differentiation trajectories of goat mammary epithelial cells, along with the gene regulatory networks and intercellular communication mechanisms governing these trajectories, thereby
Xiaoru Yan, Xiaoyu Mi, Guanghui Tan, Xinmei Li, Zhenliang Zhu, Zhenyu Wei, Huimei Fan, Yamei Wu, Tao Shi, Lingzhao Fang, Yuanpeng Gao, Yu Wang, Jun Liu   +12 more
wiley   +1 more source

Non-Stationary Acceleration Strategies for PageRank Computing

Mathematics, 2019
In this work, a non-stationary technique based on the Power method for accelerating the parallel computation of the PageRank vector is proposed and its theoretical convergence analyzed.
Héctor Migallón, Violeta Migallón, José Penadés   +2 more
doaj   +1 more source

MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi‐Omics Data

Advanced Science, EarlyView.
MAPA transforms complex multi‐omics data into biologically coherent functional modules by integrating pathway information with molecular interaction networks. Retrieval‐augmented large language models then generate structured, literature‐informed interpretations.
Yifei Ge, Feifan Zhang, Yijiang Liu, Chao Jiang, Peng Gao, Nguan Soon Tan, Sai Zhang, Yuchen Shen, Qianyi Zhou, Xin Zhou, Xiao Wang, Fangqing Zhao, Chuchu Wang, Xiaotao Shen   +13 more
wiley   +1 more source

puzzlef/pagerank-ordered-vs-unordered: Comparison of ordered vs unordered vertex processing in PageRank algorithm for link analysis

, 2022
Comparison of ordered vs unordered vertex processing in PageRank algorithm for link analysis. We generally compute PageRank by initialize the rank of each vertex (to say 1/N, where N is the total number of vertices in the graph), and iteratively updating
Subhajit Sahu
core   +1 more source

Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery

Advanced Science, EarlyView.
DRIVE integrates PPI topology and pan‐cancer multi‐omics profiles through dual‐view graph disentanglement, contrastive representation learning, and joint optimization. Across six PPI networks, DRIVE outperforms ten baselines and remains robust to structural and annotation perturbations.
Xinjing Gong, Ji Li, Mu Su, Peishen Yu, Te Ma, Ruiyang Zhai, Chenye Zhang, Mengyan Zhang, Yan Zhang   +8 more
wiley   +1 more source

New versions of PageRank employing alternative Web document models [PDF]

, 2004
This is an accepted manuscript of an article published by Emerald Group Publishing Limited in Aslib Proceedings on 01/01/2004, available online: https://doi.org/10.1108/00012530410516840 The accepted version of the publication may differ from the ...
Vaughan, Liwen, Liwen Vaughan, Mike Thelwall, Thelwall, Mike, Vaughan, L., Thelwall, M.   +5 more
core   +1 more source

puzzlef/pagerank-cuda-optimization-skip-converged: Performance benefit of skipping converged vertices for CUDA based PageRank

, 2021
Part of the report STIC-D based Algorithmic Optimizations for Monolithic PageRank. -- Performance benefit of skipping converged vertices for CUDA based PageRank (pull, CSR).
Subhajit Sahu
core   +1 more source