We introduce and study random bipartite networks with hidden variables. Nodes in these networks are characterized by hidden variables that control the appearance of links between node pairs. We derive analytic expressions for the degree distribution, degree correlations, the distribution of the number of common neighbors, and the bipartite clustering coefficient in these networks. We also establish the relationship between degrees of nodes in original bipartite networks and in their unipartite projections. We further demonstrate how hidden variable formalism can be applied to analyze topological properties of networks in certain bipartite network models, and verify our analytical results in numerical simulations.
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http://dx.doi.org/10.1103/PhysRevE.84.026114 | DOI Listing |
Front Med (Lausanne)
December 2024
Chongqing Key Laboratory of Prevention and Treatment on Major Blinding Diseases, Chongqing Eye Institute, Chongqing Branch (Municipality Division) of National Clinical Research Center for Ocular Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: This study aimed to comprehensively explore the thickness and topographic distributions of retinal vessel alterations of different myopic eyes by using swept-source OCT angiography (SS-OCTA).
Methods: One hundred myopes were included in this observational cross-sectional study. All participants underwent a series of ocular examinations of biometrical parameters, including spherical equivalent refraction (SER), axial length (AL), intraocular pressure (IOP), curvature radius (CR), and others.
Psychiatry Investig
December 2024
Department of Psychiatry, Korea University College of Medicine & Anam Hospital, Seoul, Republic of Korea.
Objective: It takes significant time and energy to collect data on explicit networks. This study used graph machine learning to identify hidden networks and predict mental health conditions in the middle-aged and old.
Methods: Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 2,000 participants aged 56 or more.
Trop Anim Health Prod
January 2025
Faculty of Agriculture, Department of Animal Science, Isparta University of Applied Sciences, Isparta, Türkiye.
The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), body height (BH), chest width (CW), and hip width (HW). Additionally, this study also investigated which machine learning algorithms could accurately and efficiently predict body weight in pigs using a limited set of morphological traits. For this purpose, morphological measurements of 365 mature (180 ± 5 days) Sujiang pigs from the Jiangsu Sujiang Pig Breeding Farm in Taizhou, Jiangsu Province, China were used.
View Article and Find Full Text PDFPLoS One
January 2025
School of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, Jiangsu, China.
ISA Trans
December 2024
School of Mathematics and Statistics, Guangxi Normal University, Guilin 541006, China. Electronic address:
This paper addresses the event-based sliding mode control problem for singularly perturbed systems with switching parameters. Unlike traditional Markovian switching systems, singularly perturbed S-MSSs allow more flexible state transitions, which can be described by a general distribution rather than the exponential distribution assumed in Markovian switching systems. To enhance the performance of such systems, a novel memory-based dynamic event-triggered protocol (DETP) is proposed, incorporating a memory term for the auxiliary offset variable.
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