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Автор: Grafiati

Опубліковано: 1 червня 2024

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1

Ndaoud, Mohamed, Suzanne Sigalla, and Alexandre B. Tsybakov. "Improved Clustering Algorithms for the Bipartite Stochastic Block Model." IEEE Transactions on Information Theory 68, no. 3 (March 2022): 1960–75. http://dx.doi.org/10.1109/tit.2021.3130683.

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2

Bolla, Marianna, and Ahmed Elbanna. "Estimating Parameters of a Probabilistic Heterogeneous Block Model via the EM Algorithm." Journal of Probability and Statistics 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/657965.

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We introduce a semiparametric block model for graphs, where the within- and between-cluster edge probabilities are not constants within the blocks but are described by logistic type models, reminiscent of the 50-year-old Rasch model and the newly introducedα-βmodels. Our purpose is to give a partition of the vertices of an observed graph so that the induced subgraphs and bipartite graphs obey these models, where their strongly interlaced parameters give multiscale evaluation of the vertices at the same time. In this way, a profoundly heterogeneous version of the stochastic block model is built via mixtures of the above submodels, while the parameters are estimated with a special EM iteration.

3

Wang, Guo-Zheng, Li Xiong, and Hu-Chen Liu. "A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks." Scientific Programming 2019 (December 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/9471201.

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Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

4

Wang, Yurun, Pu Zhao, Senkai Xie, and Wenjia Zhang. "Mesoscale Structure in Urban–Rural Mobility Networks in the Pearl River Delta Area: A Weighted Stochastic Block Modeling Analysis." ISPRS International Journal of Geo-Information 12, no. 5 (April 27, 2023): 183. http://dx.doi.org/10.3390/ijgi12050183.

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Understanding the spatial structure of a megaregion with urban and rural areas is crucial for promoting sustainable urbanization and urban–rural integration. Compared to the city network (or the network of urban areas), however, fewer studies focus on the network connecting rural areas or on the comparison of regional structures between urban and rural networks. Using weighted daily mobility flows from the massive mobile-phone signaling data, this study constructs an urban–urban mobility (UUM) network and an urban–rural mobility (URM) network in the Pearl River Delta (PRD) region. A weighted stochastic block model (WSBM) was adopted to identify and compare the latent mesoscale structures in the two networks. Results investigated a gradient community mesoscale structure nested with typical core–periphery (CP) structures in the UUM network and an asymmetric bipartite mesoscale structure mixed with CP hierarchies in the URM network. In a comparison of the different spatial configuration of urban/rural nodes and groupings of their roles, positions, and linkages, the study yielded empirical insights for renewed urban–rural interaction and potential planning pathways towards urban–rural integration.

5

Balzer, Laura, Patrick Staples, Jukka-Pekka Onnela, and Victor DeGruttola. "Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial." Clinical Trials 14, no. 2 (January 26, 2017): 201–10. http://dx.doi.org/10.1177/1740774516679666.

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Background: Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV– persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat and pre-exposure prophylaxis strategies. Methods: We simulated 1000 trials, each consisting of 32 villages with 200 individuals per village. At baseline, we randomized the universal test-and-treat strategy. Then, after 3 years of follow-up, we considered four strategies for targeting pre-exposure prophylaxis: (1) all HIV– individuals who self-identify as high risk, (2) all HIV– individuals who are identified by their HIV+ partner (serodiscordant couples), (3) highly connected HIV– individuals, and (4) the HIV– contacts of a newly diagnosed HIV+ individual (a ring-based strategy). We explored two possible trial designs, and all villages were followed for a total of 7 years. For each village in a trial, we used a stochastic block model to generate bipartite (male–female) networks and simulated an agent-based epidemic process on these networks. We estimated the individual and combined intervention effects with a novel targeted maximum likelihood estimator, which used cross-validation to data-adaptively select from a pre-specified library the candidate estimator that maximized the efficiency of the analysis. Results: The universal test-and-treat strategy reduced the 3-year cumulative HIV incidence by 4.0% on average. The impact of each pre-exposure prophylaxis strategy on the 4-year cumulative HIV incidence varied by the coverage of the universal test-and-treat strategy with lower coverage resulting in a larger impact of pre-exposure prophylaxis. Offering pre-exposure prophylaxis to serodiscordant couples resulted in the largest reductions in HIV incidence (2% reduction), and the ring-based strategy had little impact (0% reduction). The joint effect was larger than either individual effect with reductions in the 7-year incidence ranging from 4.5% to 8.8%. Targeted maximum likelihood estimation, data-adaptively adjusting for baseline covariates, substantially improved power over the unadjusted analysis, while maintaining nominal confidence interval coverage. Conclusion: Our simulation study suggests that nesting a pre-exposure prophylaxis study within an ongoing trial can lead to combined intervention effects greater than those of universal test-and-treat alone and can provide information about the efficacy of pre-exposure prophylaxis in the presence of high coverage of treatment for HIV+ persons.

6

Xu, Zhijuan, Xueyan Liu, Xianjuan Cui, Ximing Li, and Bo Yang. "Robust stochastic block model." Neurocomputing 379 (February 2020): 398–412. http://dx.doi.org/10.1016/j.neucom.2019.10.069.

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7

Wu, Xunxun, Chang-Dong Wang, and Pengfei Jiao. "Hybrid-order Stochastic Block Model." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4470–77. http://dx.doi.org/10.1609/aaai.v35i5.16574.

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Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches.

8

Zhang, Yun, Kehui Chen, Allan Sampson, Kai Hwang, and Beatriz Luna. "Node Features Adjusted Stochastic Block Model." Journal of Computational and Graphical Statistics 28, no. 2 (February 27, 2019): 362–73. http://dx.doi.org/10.1080/10618600.2018.1530117.

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9

Zhao, Feng, Min Ye, and Shao-Lun Huang. "Exact Recovery of Stochastic Block Model by Ising Model." Entropy 23, no. 1 (January 2, 2021): 65. http://dx.doi.org/10.3390/e23010065.

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In this paper, we study the phase transition property of an Ising model defined on a special random graph—the stochastic block model (SBM). Based on the Ising model, we propose a stochastic estimator to achieve the exact recovery for the SBM. The stochastic algorithm can be transformed into an optimization problem, which includes the special case of maximum likelihood and maximum modularity. Additionally, we give an unbiased convergent estimator for the model parameters of the SBM, which can be computed in constant time. Finally, we use metropolis sampling to realize the stochastic estimator and verify the phase transition phenomenon thfough experiments.

10

Moyal, Pascal, Ana Bušić, and Jean Mairesse. "A product form for the general stochastic matching model." Journal of Applied Probability 58, no. 2 (June 2021): 449–68. http://dx.doi.org/10.1017/jpr.2020.100.

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AbstractWe consider a stochastic matching model with a general compatibility graph, as introduced by Mairesse and Moyal (2016). We show that the natural necessary condition of stability of the system is also sufficient for the natural ‘first-come, first-matched’ matching policy. To do so, we derive the stationary distribution under a remarkable product form, by using an original dynamic reversibility property related to that of Adan, Bušić, Mairesse, and Weiss (2018) for the bipartite matching model.

11

Arcuri, Alesandro, and Nicolas Lanchier. "Stochastic spatial model for the division of labor in social insects." Mathematical Models and Methods in Applied Sciences 27, no. 01 (January 2017): 45–73. http://dx.doi.org/10.1142/s0218202517400024.

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Motivated by the study of social insects, we introduce a stochastic model based on interacting particle systems in order to understand the effect of communication on the division of labor. Members of the colony are located on the vertex set of a graph representing a communication network. They are characterized by one of two possible tasks, which they update at a rate equal to the cost of the task they are performing by either defecting by switching to the other task or cooperating by anti-imitating a random neighbor in order to balance the amount of energy spent in each task. We prove that, at least when the probability of defection is small, the division of labor is poor when there is no communication, better when the communication network consists of a complete graph, but optimal on bipartite graphs with bipartite sets of equal size, even when both tasks have very different costs. This shows a non-monotonic relationship between the number of connections in the communication network and how well individuals organize themselves to accomplish both tasks equally.

12

Abbe, Emmanuel, Afonso S. Bandeira, and Georgina Hall. "Exact Recovery in the Stochastic Block Model." IEEE Transactions on Information Theory 62, no. 1 (January 2016): 471–87. http://dx.doi.org/10.1109/tit.2015.2490670.

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13

Lelarge, Marc, Laurent Massoulie, and Jiaming Xu. "Reconstruction in the Labelled Stochastic Block Model." IEEE Transactions on Network Science and Engineering 2, no. 4 (October 1, 2015): 152–63. http://dx.doi.org/10.1109/tnse.2015.2490580.

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14

Rastelli, Riccardo, and Michael Fop. "A stochastic block model for interaction lengths." Advances in Data Analysis and Classification 14, no. 2 (June 2020): 485–512. http://dx.doi.org/10.1007/s11634-020-00403-w.

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15

Latouche, Pierre, Etienne Birmelé, and Christophe Ambroise. "Model selection in overlapping stochastic block models." Electronic Journal of Statistics 8, no. 1 (2014): 762–94. http://dx.doi.org/10.1214/14-ejs903.

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16

Sun, Ya, Meiyi Wang, and Hua Xie. "Volatility analysis of the flight block time based on the stochastic volatility model." Journal of Physics: Conference Series 2489, no. 1 (May 1, 2023): 012002. http://dx.doi.org/10.1088/1742-6596/2489/1/012002.

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Abstract To effectively predict the volatility of flight block time, this paper constructs a stochastic volatility model based on actual flight block time data, solves the model parameters by the Markov chain Monte Carlo method, and uses the standard stochastic volatility (SV-N) model and thick-tailed stochastic volatility (SV-T) model to characterize the volatility of flight block time. The results show that the thick-tailed stochastic volatility model is better than the standard stochastic volatility model in describing the volatility of the segment runtime, and the thick-tailed stochastic volatility model is chosen to predict the volatility of the flight block time. Predicting the flight block time volatility in real time can provide a theoretical basis for traffic traveler planning.

17

Li, Yang, Hechang Chen, and Bo Yang. "Reparameterized Stochastic Block Model Adaptive to Heterogeneous Degree and Block Distributions." IEEE Access 6 (2018): 37615–26. http://dx.doi.org/10.1109/access.2018.2853115.

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18

Neal, Zachary P., and Jennifer Watling Neal. "Illustrating the importance of edge constraints in backbones of bipartite projections." PLOS ONE 19, no. 5 (May 10, 2024): e0302973. http://dx.doi.org/10.1371/journal.pone.0302973.

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Bipartite projections (e.g., event co-attendance) are often used to measure unipartite networks of interest (e.g., social interaction). Backbone extraction models can be useful for reducing the noise inherent in bipartite projections. However, these models typically assume that the bipartite edges (e.g., who attended which event) are unconstrained, which may not be true in practice (e.g., a person cannot attend an event held prior to their birth). We illustrate the importance of correctly modeling such edge constraints when extracting backbones, using both synthetic data that varies the number and type of constraints, and empirical data on children’s play groups. We find that failing to impose relevant constraints when the data contain constrained edges can result in the extraction of an inaccurate backbone. Therefore, we recommend that when bipartite data contain constrained edges, backbones be extracted using a model such as the Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC).

19

Becker, Ann-Kristin, and Hajo Holzmann. "Nonparametric Identification in the Dynamic Stochastic Block Model." IEEE Transactions on Information Theory 65, no. 7 (July 2019): 4335–44. http://dx.doi.org/10.1109/tit.2019.2893947.

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20

Caltagirone, Francesco, Marc Lelarge, and Leo Miolane. "Recovering Asymmetric Communities in the Stochastic Block Model." IEEE Transactions on Network Science and Engineering 5, no. 3 (July 1, 2018): 237–46. http://dx.doi.org/10.1109/tnse.2017.2758201.

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21

Wang, Y. X. Rachel, and Peter J. Bickel. "Likelihood-based model selection for stochastic block models." Annals of Statistics 45, no. 2 (April 2017): 500–528. http://dx.doi.org/10.1214/16-aos1457.

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22

Pensky, Marianna, and Teng Zhang. "Spectral clustering in the dynamic stochastic block model." Electronic Journal of Statistics 13, no. 1 (2019): 678–709. http://dx.doi.org/10.1214/19-ejs1533.

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23

Chen, Haoran, Zhongjing Yu, Qinli Yang, and Junming Shao. "Attributed graph clustering with subspace stochastic block model." Information Sciences 535 (October 2020): 130–41. http://dx.doi.org/10.1016/j.ins.2020.05.044.

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24

Coulson, Matthew, Robert E. Gaunt, and Gesine Reinert. "Compound Poisson approximation of subgraph counts in stochastic block models with multiple edges." Advances in Applied Probability 50, no. 3 (September 2018): 759–82. http://dx.doi.org/10.1017/apr.2018.35.

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Abstract We use the Stein‒Chen method to obtain compound Poisson approximations for the distribution of the number of subgraphs in a generalised stochastic block model which are isomorphic to some fixed graph. This model generalises the classical stochastic block model to allow for the possibility of multiple edges between vertices. We treat the case that the fixed graph is a simple graph and that it has multiple edges. The former results apply when the fixed graph is a member of the class of strictly balanced graphs and the latter results apply to a suitable generalisation of this class to graphs with multiple edges. We also consider a further generalisation of the model to pseudo-graphs, which may include self-loops as well as multiple edges, and establish a parameter regime in the multiple edge stochastic block model in which Poisson approximations are valid. The results are applied to obtain Poisson and compound Poisson approximations (in different regimes) for subgraph counts in the Poisson stochastic block model and degree corrected stochastic block model of Karrer and Newman (2011).

25

Pal, Soumik, and Yizhe Zhu. "Community detection in the sparse hypergraph stochastic block model." Random Structures & Algorithms 59, no. 3 (March 14, 2021): 407–63. http://dx.doi.org/10.1002/rsa.21006.

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26

Anastos, Michael, Alan Frieze, and Pu Gao. "Hamiltonicity of Random Graphs in the Stochastic Block Model." SIAM Journal on Discrete Mathematics 35, no. 3 (January 2021): 1854–80. http://dx.doi.org/10.1137/19m1296069.

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27

Lu, Y. N., and E. J. Ding. "Self-organized criticality in a stochastic spring-block model." Physical Review E 48, no. 1 (July 1, 1993): R21—R24. http://dx.doi.org/10.1103/physreve.48.r21.

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28

Srinivas, B., S. Gajanana, and K. Hemachandra Reddy. "Forecasted Inflation Based Block Replacement Model Using Stochastic Process." Applied Mechanics and Materials 592-594 (July 2014): 2716–22. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.2716.

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The replacement problems are concerned with the situation that arises on decrease in the efficiency of the item, failure or breakdown. The problem of replacement is to identify the best policy to determine the ideal replacement time which is most economical. Group replacement model is applicable to the items that fail completely on usage and the result is group replacement age for the entire group of items in the system irrespective of whether they are functioning or not. The present paper proposes intermediate states i.e., minor repair and major repair states in between functioning and irreparable breakdown states. In addition, higher order Markov chains are used in generating the probabilities of items which are falling in different states. In order to consider money value, macro-economic variable, inflation is considered in this model. In the present model, real interest rates are calculated using forecasted inflation for future periods. Future period values of inflation are predicted by using the forecasting technique and a regression model with trigonometric function. These methods are used to accommodate cyclical fluctuation in the prices of items/inflation. The optimal replacement age is the time bucket in which the average cost of the individual replacement, repair and the cost of the items is minimum.

29

Ahn, Kwangjun, Kangwook Lee, and Changho Suh. "Hypergraph Spectral Clustering in the Weighted Stochastic Block Model." IEEE Journal of Selected Topics in Signal Processing 12, no. 5 (October 2018): 959–74. http://dx.doi.org/10.1109/jstsp.2018.2837638.

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30

Xin, Lu, Mu Zhu, and Hugh Chipman. "A continuous-time stochastic block model for basketball networks." Annals of Applied Statistics 11, no. 2 (June 2017): 553–97. http://dx.doi.org/10.1214/16-aoas993.

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31

Li, Shuping, and Xiaorong Zhao. "Network percolation of the disease transmission based on bipartite networks." International Journal of Modern Physics B 34, no. 06 (February 24, 2020): 2050029. http://dx.doi.org/10.1142/s0217979220500290.

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In this paper, considered heterogeneous infectivity and susceptibility, a general stochastic Susceptible-Infectious-Removed (SIR) epidemic model with the cumulative distribution functions (CDFS) of the infectious contact rate and the infectious period based on bipartite networks is discussed. It is isomorphic to a semidirected random network called the bipartite epidemic percolation network. The epidemic threshold corresponds to the phase transition where a giant strongly connected component appears. It is obtained by using the method of the probability generation function. We show that the critical value of the transmissibility predicted by the bond percolation model is larger than that predicted by the epidemic percolation network. We analyze the influences of the network structure and individual heterogeneity on the epidemic threshold by numerical simulations.

32

Graczyk, Małgorzata, and Bronisław Ceranka. "A Regular D‑optimal Weighing Design with Negative Correlations of Errors." Acta Universitatis Lodziensis. Folia Oeconomica 5, no. 344 (September 30, 2019): 7–16. http://dx.doi.org/10.18778/0208-6018.344.01.

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The issues concerning optimal estimation of unknown parameters in the model of chemical balance weighing designs with negative correlated errors are considered. The necessary and sufficient conditions determining the regular D‑optimal design and some new construction methods are presented. They are based on the incidence matrices of balanced incomplete block designs and balanced bipartite weighing designs.

33

Auconi, Andrea, Andrea Giansanti, and Edda Klipp. "Information Thermodynamics for Time Series of Signal-Response Models." Entropy 21, no. 2 (February 14, 2019): 177. http://dx.doi.org/10.3390/e21020177.

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The entropy production in stochastic dynamical systems is linked to the structure of their causal representation in terms of Bayesian networks. Such a connection was formalized for bipartite (or multipartite) systems with an integral fluctuation theorem in [Phys. Rev. Lett. 111, 180603 (2013)]. Here we introduce the information thermodynamics for time series, that are non-bipartite in general, and we show that the link between irreversibility and information can only result from an incomplete causal representation. In particular, we consider a backward transfer entropy lower bound to the conditional time series irreversibility that is induced by the absence of feedback in signal-response models. We study such a relation in a linear signal-response model providing analytical solutions, and in a nonlinear biological model of receptor-ligand systems where the time series irreversibility measures the signaling efficiency.

34

Wu, Huaying, Luoyi Fu, Huan Long, Guie Meng, Xiaoying Gan, Yuanhao Wu, Haisong Zhang, and Xinbing Wang. "Unraveling the Detectability of Stochastic Block Model With Overlapping Communities." IEEE Transactions on Network Science and Engineering 8, no. 2 (April 1, 2021): 1443–55. http://dx.doi.org/10.1109/tnse.2021.3058520.

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35

Ye, Min. "Exact Recovery and Sharp Thresholds of Stochastic Ising Block Model." IEEE Transactions on Information Theory 67, no. 12 (December 2021): 8207–35. http://dx.doi.org/10.1109/tit.2021.3117264.

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36

Ye, Min. "Exact Recovery and Sharp Thresholds of Stochastic Ising Block Model." IEEE Transactions on Information Theory 67, no. 12 (December 2021): 8207–35. http://dx.doi.org/10.1109/tit.2021.3117264.

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37

CHAI, Bian-Fang, Jian YU, Cai-Yan JIA, and Jing-Hong WANG. "Fast Algorithm on Stochastic Block Model for Exploring General Communities." Journal of Software 24, no. 11 (January 3, 2014): 2699–709. http://dx.doi.org/10.3724/sp.j.1001.2013.04474.

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38

Stanley, Natalie, Saray Shai, Dane Taylor, and Peter J. Mucha. "Clustering Network Layers with the Strata Multilayer Stochastic Block Model." IEEE Transactions on Network Science and Engineering 3, no. 2 (April 1, 2016): 95–105. http://dx.doi.org/10.1109/tnse.2016.2537545.

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39

Zhang, Yue, and Mingao Yuan. "Nonreconstruction of high-dimensional stochastic block model with bounded degree." Statistics & Probability Letters 158 (March 2020): 108675. http://dx.doi.org/10.1016/j.spl.2019.108675.

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40

Qing, Huan, and Jingli Wang. "Regularized spectral clustering under the mixed membership stochastic block model." Neurocomputing 550 (September 2023): 126490. http://dx.doi.org/10.1016/j.neucom.2023.126490.

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41

Spaulding, Travis, Nicholas Strayer, Andrew Sochacki, Shannon Stockton, Alexander Silver, Rodney Dixon Dorand, Siwei Zhang, Ya-Chen Lin, Yaomin Xu, and Michael R. Savona. "Patient-Specific Risk Factors Independently Influence Survival in Myelodysplastic Syndromes in an Unbiased Review of EHR Records." Blood 134, Supplement_1 (November 13, 2019): 5440. http://dx.doi.org/10.1182/blood-2019-122400.

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Background: Myelodysplastic syndromes (MDS) are clonal hematologic neoplasms stratified by risk by the international prognostic scoring system (IPSS) and IPSS-revised (IPSS-R) which measure risk by morphologic dysplasia, clinical cytopenias, blast count, and cytogenetic abnormalities. (PMID: 9058730, 22740453) The IPSS/IPSS-R do not consider clinical comorbid conditions, though MDS patients with higher burden of comorbid disease have higher rates of non-leukemic death, particularly those with cardiovascular and pulmonary disease. (19324411) Despite this, there has been limited investigation into how specific comorbid conditions may help define subgroups of patients with MDS. Methods: We identified 2676 cases of MDS as defined by ICD-9 code (238.72 - 238.75) in Vanderbilt's Synthetic Derivative (SD). The SD is a de-identified electronic health record (EHR) of over 2.2 million patients with a companion biorepository of DNA (BioVU) for a subset of these patients, including all of the patients with MDS. The 2676 cases were matched by age, gender, race, burden of comorbidities in EHR, and age at last appointment in EHR with 5287 controls. ICD-9 codes for other myeloid disease (e.g., myeloproliferative neoplasms, acute myeloid leukemia) or history of hematopoietic stem cell transplant were excluded among the controls. Characterization of comorbidities, via phecode analysis, was conducted on all cases and controls. Phecodes are groups of related ICD-9 codes describing a clinical syndrome or medical problem, previously demonstrated to be useful in phenome-wide associated studies in EHRs. (28686612) A case was defined as having a phecode only if a representative ICD-9 code was present on two distinct days in the EHR. Next, a cluster analysis of the study population and their associated comorbidities, via a bipartite stochastic block model, was completed, and the study population was organized into hierarchical structure based upon the similarities in comorbidity patterns among patients. Results: ICD-9 codes from the study population made up 181 phecodes, which were found in hierarchical cluster analysis to further cluster into 54 sub-groups and 16 larger groups. MDS patients clustered throughout all groups, the majority of which contained control patients; yet some MDS cases sub-clustered into groups that included a majority of MDS cases and these were further analyzed. Notably, two groups had equivalent size and MDS status were found to have significant differences in phecode profiles. Group 1 had 795 total patients with 783 MDS cases (98.5%) and Group 2 had 769 total patients with 684 MDS cases (88.9%), as per Fig 1a. There were no significant difference in sex between the two groups. Group 1 patients were significantly younger than Group 2 patients (58.3y vs 62.9y; p = 1.36 x 10-7), yet tended have increased risk of renal, cardiovascular and thromboembolic disease than Group 2, as per Fig 1b. Additionally, a higher proportion of Group 2 patients (695/769 or 90.4%) were alive at time of data extraction than Group 1 patients (451/795 or 56.7%) (OR 4.51, p = <2.2 x 10-16). Conclusions: By performing a phenome-wide analysis of patients with MDS in a large electronic health record (EHR), we reveal specific subgroups of MDS patients with distinct comorbidities and different survival, not affected by age or sex. This study demonstrates the ability to study comorbid conditions of MDS patients in an unbiased fashion, independent of disease specific risk factors that inform IPSS-R and which have historically been most important in stratifying risk in MDS. The role of comorbidity is instinctually clear to the adroit clinician, and this technique could provide distinct comorbid disease patterns which impute risk, or perhaps etiology in MDS. Disclosures Savona: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Selvita: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sunesis: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Patents & Royalties; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees.

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Han, Jie, Tao Guo, Qiaoqiao Zhou, Wei Han, Bo Bai, and Gong Zhang. "Structural Entropy of the Stochastic Block Models." Entropy 24, no. 1 (January 3, 2022): 81. http://dx.doi.org/10.3390/e24010081.

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With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the “structural information” only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős–Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the stochastic block models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for stochastic block models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the stochastic block models, and provide a compression scheme that asymptotically achieves this entropy limit.

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Penocchio, Emanuele, Francesco Avanzini, and Massimiliano Esposito. "Information thermodynamics for deterministic chemical reaction networks." Journal of Chemical Physics 157, no. 3 (July 21, 2022): 034110. http://dx.doi.org/10.1063/5.0094849.

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Information thermodynamics relates the rate of change of mutual information between two interacting subsystems to their thermodynamics when the joined system is described by a bipartite stochastic dynamics satisfying local detailed balance. Here, we expand the scope of information thermodynamics to deterministic bipartite chemical reaction networks, namely, composed of two coupled subnetworks sharing species but not reactions. We do so by introducing a meaningful notion of mutual information between different molecular features that we express in terms of deterministic concentrations. This allows us to formulate separate second laws for each subnetwork, which account for their energy and information exchanges, in complete analogy with stochastic systems. We then use our framework to investigate the working mechanisms of a model of chemically driven self-assembly and an experimental light-driven bimolecular motor. We show that both systems are constituted by two coupled subnetworks of chemical reactions. One subnetwork is maintained out of equilibrium by external reservoirs (chemostats or light sources) and powers the other via energy and information flows. In doing so, we clarify that the information flow is precisely the thermodynamic counterpart of an information ratchet mechanism only when no energy flow is involved.

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Vo, Thi Phuong Thuy. "Chain-referral sampling on stochastic block models." ESAIM: Probability and Statistics 24 (2020): 718–38. http://dx.doi.org/10.1051/ps/2020025.

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The discovery of the “hidden population”, whose size and membership are unknown, is made possible by assuming that its members are connected in a social network by their relationships. We explore these groups by a chain-referral sampling (CRS) method, where participants recommend the people they know. This leads to the study of a Markov chain on a random graph where vertices represent individuals and edges connecting any two nodes describe the relationships between corresponding people. We are interested in the study of CRS process on the stochastic block model (SBM), which extends the well-known Erdös-Rényi graphs to populations partitioned into communities. The SBM considered here is characterized by a number of vertices N, a number of communities (blocks) m, proportion of each community π = (π1, …, πm) and a pattern for connection between blocks P = (λkl∕N)(k,l)∈{1,…,m}2. In this paper, we give a precise description of the dynamic of CRS process in discrete time on an SBM. The difficulty lies in handling the heterogeneity of the graph. We prove that when the population’s size is large, the normalized stochastic process of the referral chain behaves like a deterministic curve which is the unique solution of a system of ODEs.

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Ceranka, Bronisław, and Małgorzata Graczyk. "New Results Regarding the Construction Method for D‑optimal Chemical Balance Weighing Designs." Acta Universitatis Lodziensis. Folia Oeconomica 4, no. 349 (November 23, 2020): 129–41. http://dx.doi.org/10.18778/0208-6018.349.08.

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We study an experiment in which we determine unknown measurements of p objects in n weighing operations according to the model of the chemical balance weighing design. We determine a design which is D‑optimal. For the construction of the D‑optimal design, we use the incidence matrices of balance incomplete block designs, balanced bipartite weighing designs and ternary balanced block designs. We give some optimality conditions determining the relationships between the parameters of a D‑optimal design and we present a series of parameters of such designs. Based on these parameters, we will be able to set down D‑optimal designs in classes in which it was impossible so far.

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Vasovic, Nebojsa, Srdjan Kostic, Kristina Todorovic, and Dragoslav Kuzmanovic. "Synchronization conditions for stochastic landslide chain model with delayed coupling." Theoretical and Applied Mechanics, no. 00 (2024): 1. http://dx.doi.org/10.2298/tam230927001v.

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We examine the conditions for synchronization of landslide stochastic chain model with delayed coupling. Firstly, a new chain model for landslide dynamics is proposed, with the included effect of delayed coupling and background noise. The model is of the microscopic type, where the state of each block in the chain is influenced by the previous state of the same block and its neighbors as well as by noise. Secondly, we examine the stochastic synchronization of such a system of stochastic delay-differential equations. A sufficient condition for the exponential mean square stability of the synchronization is obtained. The sufficient condition indicates that the uni-directional asymmetric coupling induces the synchronization much more efficiently than the bi-directionally symmetric one. From the practical viewpoint, the results obtained confirm that different parts of the large unstable slope could exhibit synchronized activity under certain conditions, which indicates their possible larger influence on the structures (and generation of corresponding deformation) compared to the individual effect of unsynchronized activities.

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Kataoka, Shun, Takuto Kobayashi, Muneki Yasuda, and Kazuyuki Tanaka. "Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering." Journal of the Physical Society of Japan 85, no. 11 (November 15, 2016): 114802. http://dx.doi.org/10.7566/jpsj.85.114802.

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48

Cerqueira, Andressa, and Florencia Leonardi. "Estimation of the Number of Communities in the Stochastic Block Model." IEEE Transactions on Information Theory 66, no. 10 (October 2020): 6403–12. http://dx.doi.org/10.1109/tit.2020.3016331.

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49

Matias, Catherine, and Vincent Miele. "Statistical clustering of temporal networks through a dynamic stochastic block model." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79, no. 4 (August 22, 2016): 1119–41. http://dx.doi.org/10.1111/rssb.12200.

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50

Xu, Min, Varun Jog, and Po-Ling Loh. "Optimal rates for community estimation in the weighted stochastic block model." Annals of Statistics 48, no. 1 (February 2020): 183–204. http://dx.doi.org/10.1214/18-aos1797.

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