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Artykuły w czasopismach na temat "Bipartite stochastic block model"
Ndaoud, Mohamed, Suzanne Sigalla i Alexandre B. Tsybakov. "Improved Clustering Algorithms for the Bipartite Stochastic Block Model". IEEE Transactions on Information Theory 68, nr 3 (marzec 2022): 1960–75. http://dx.doi.org/10.1109/tit.2021.3130683.
Pełny tekst źródłaBolla, Marianna, i 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.
Pełny tekst źródłaWang, Guo-Zheng, Li Xiong i Hu-Chen Liu. "A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks". Scientific Programming 2019 (9.12.2019): 1–12. http://dx.doi.org/10.1155/2019/9471201.
Pełny tekst źródłaWang, Yurun, Pu Zhao, Senkai Xie i 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, nr 5 (27.04.2023): 183. http://dx.doi.org/10.3390/ijgi12050183.
Pełny tekst źródłaBalzer, Laura, Patrick Staples, Jukka-Pekka Onnela i 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, nr 2 (26.01.2017): 201–10. http://dx.doi.org/10.1177/1740774516679666.
Pełny tekst źródłaXu, Zhijuan, Xueyan Liu, Xianjuan Cui, Ximing Li i Bo Yang. "Robust stochastic block model". Neurocomputing 379 (luty 2020): 398–412. http://dx.doi.org/10.1016/j.neucom.2019.10.069.
Pełny tekst źródłaWu, Xunxun, Chang-Dong Wang i Pengfei Jiao. "Hybrid-order Stochastic Block Model". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 5 (18.05.2021): 4470–77. http://dx.doi.org/10.1609/aaai.v35i5.16574.
Pełny tekst źródłaZhang, Yun, Kehui Chen, Allan Sampson, Kai Hwang i Beatriz Luna. "Node Features Adjusted Stochastic Block Model". Journal of Computational and Graphical Statistics 28, nr 2 (27.02.2019): 362–73. http://dx.doi.org/10.1080/10618600.2018.1530117.
Pełny tekst źródłaZhao, Feng, Min Ye i Shao-Lun Huang. "Exact Recovery of Stochastic Block Model by Ising Model". Entropy 23, nr 1 (2.01.2021): 65. http://dx.doi.org/10.3390/e23010065.
Pełny tekst źródłaMoyal, Pascal, Ana Bušić i Jean Mairesse. "A product form for the general stochastic matching model". Journal of Applied Probability 58, nr 2 (czerwiec 2021): 449–68. http://dx.doi.org/10.1017/jpr.2020.100.
Pełny tekst źródłaRozprawy doktorskie na temat "Bipartite stochastic block model"
Sigalla, Suzanne. "Contributions to structured high-dimensional inference". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG013.
Pełny tekst źródłaIn this thesis, we consider the three following problems: clustering in Bipartite Stochastic Block Model, estimation of topic-document matrix in topic model, and benign overfitting in nonparametric regression. First, we consider the graph clustering problem in the Bipartite Stochastic Block Model (BSBM). The BSBM is a non-symmetric generalization of the Stochastic Block Model, with two sets of vertices. We provide an algorithm called the Hollowed Lloyd's algorithm, which allows one to classify vertices of the smallest set with high probability. We provide statistical guarantees on this algorithm, which is computationnally fast and simple to implement. We establish a sufficient condition for clustering in BSBM. Our results improve on previous works on BSBM, in particular in the high-dimensional regime. Second, we study the problem of assigning topics to documents using topic models. Topic models allow one to discover hidden structures in a large corpus of documents through dimension reduction. Each topic is considered as a probability distribution on the dictionary of words, and each document is considered as a mixture of topics. We introduce an algotihm called the Successive Projection Overlapping Clustering (SPOC) algorithm, inspired by the Successive Projection Algorithm for Non-negative Matrix Factorization. The SPOC algorithm is computationnally fast and simple to implement. We provide statistical guarantees on the outcome of the algorithm. In particular, we provide near matching minimax upper and lower bounds on its estimation risk under the Frobenius and the l1-norm. Our clustering procedure is adaptive in the number of topics. Finally, the third problem we study is a nonparametric regression problem. We consider local polynomial estimators with singular kernel, which we prove to be minimax optimal, adaptive to unknown smoothness, and interpolating with high probability. This property is called benign overfitting
Ludkin, Matthew Robert. "The autoregressive stochastic block model with changes in structure". Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/125642/.
Pełny tekst źródłaPaltrinieri, Federico. "Modeling temporal networks with dynamic stochastic block models". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18805/.
Pełny tekst źródłaVallès, Català Toni. "Network inference based on stochastic block models: model extensions, inference approaches and applications". Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/399539.
Pełny tekst źródłaEl estudio de las redes del mundo real han empujado hacia la comprensión de sistemas complejos en una amplia gama de campos como la biología molecular y celular, la anatomía, la neurociencia, la ecología, la economía y la sociología . Sin embargo, el conocimiento disponible de muchos sistemas reales aún es limitado, por esta razón el poder predictivo de la ciencia en redes se debe mejorar para disminuir la brecha entre conocimiento y información. Para abordar este tema usamos la familia de 'Stochastic Block Modelos' (SBM), una familia de modelos generativos que está ganando gran interés recientemente debido a su adaptabilidad a cualquier tipo de red. El objetivo de esta tesis es el desarrollo de nuevas metodologías de inferencia basadas en SBM que perfeccionarán nuestra comprensión de las redes complejas. En primer lugar, investigamos en qué medida hacer un muestreo sobre modelos puede mejorar significativamente la capacidad de predicción a considerar un único conjunto óptimo de parámetros. Seguidamente, aplicamos el método mas predictivo en una red real particular: una red basada en las interacciones/suturas entre los huesos del cráneo humano en recién nacidos. Concretamente, descubrimos que las suturas cerradas a causa de una enfermedad patológica en recién nacidos son menos probables, desde un punto de vista morfológico, que las suturas cerradas bajo un desarrollo normal. Concretamente, descubrimos que las suturas cerradas a causa de una enfermedad patológica en recién nacidos son menos probables, desde un punto de vista morfológico, que las suturas cerradas bajo un desarrollo normal. Recientes investigaciones en las redes multicapa concluye que el comportamiento de las redes en una sola capa son diferentes a las de múltiples capas; por otra parte, las redes del mundo real se nos presentan como redes con una sola capa. La parte final de la tesis está dedicada a diseñar un nuevo enfoque en el que dos SBM separados describen simultáneamente una red dada que consta de una sola capa, observamos que esta metodología predice mejor que la metodología de un SBM solo.
The study of real-world networks have pushed towards to the understanding of complex systems in a wide range of fields as molecular and cell biology, anatomy, neuroscience, ecology, economics and sociology. However, the available knowledge from most systems is still limited, hence network science predictive power should be enhanced to diminish the gap between knowledge and information. To address this topic we handle with the family of Stochastic Block Models (SBMs), a family of generative models that are gaining high interest recently due to its adaptability to any kind of network structure. The goal of this thesis is to develop novel SBM based inference approaches that will improve our understanding of complex networks. First, we investigate to what extent sampling over models significatively improves the predictive power than considering an optimal set of parameters alone. Once we know which model is capable to describe better a given network, we apply such method in a particular real world network case: a network based on the interactions/sutures between bones in newborn skulls. Notably, we discovered that sutures fused due to a pathological disease in human newborn were less likely, from a morphological point of view, that those sutures that fused under a normal development. Recent research on multilayer networks has concluded that the behavior of single-layered networks are different from those of multilayer ones; notwhithstanding, real world networks are presented to us as single-layered networks. The last part of the thesis is devoted to design a novel approach where two separate SBMs simultaneously describe a given single-layered network. We importantly find that it predicts better missing/spurious links that the single SBM approach.
Corneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs". Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.
Pełny tekst źródłaThis thesis focuses on the statistical analysis of dynamic graphs, both defined in discrete or continuous time. We introduce a new extension of the stochastic block model (SBM) for dynamic graphs. The proposed approach, called dSBM, adopts non homogeneous Poisson processes to model the interaction times between pairs of nodes in dynamic graphs, either in discrete or continuous time. The intensity functions of the processes only depend on the node clusters, in a block modelling perspective. Moreover, all the intensity functions share some regularity properties on hidden time intervals that need to be estimated. A recent estimation algorithm for SBM, based on the greedy maximization of an exact criterion (exact ICL) is adopted for inference and model selection in dSBM. Moreover, an exact algorithm for change point detection in time series, the "pruned exact linear time" (PELT) method is extended to deal with dynamic graph data modelled via dSBM. The approach we propose can be used for change point analysis in graph data. Finally, a further extension of dSBM is developed to analyse dynamic net- works with textual edges (like social networks, for instance). In this context, the graph edges are associated with documents exchanged between the corresponding vertices. The textual content of the documents can provide additional information about the dynamic graph topological structure. The new model we propose is called "dynamic stochastic topic block model" (dSTBM).Graphs are mathematical structures very suitable to model interactions between objects or actors of interest. Several real networks such as communication networks, financial transaction networks, mobile telephone networks and social networks (Facebook, Linkedin, etc.) can be modelled via graphs. When observing a network, the time variable comes into play in two different ways: we can study the time dates at which the interactions occur and/or the interaction time spans. This thesis only focuses on the first time dimension and each interaction is assumed to be instantaneous, for simplicity. Hence, the network evolution is given by the interaction time dates only. In this framework, graphs can be used in two different ways to model networks. Discrete time […] Continuous time […]. In this thesis both these perspectives are adopted, alternatively. We consider new unsupervised methods to cluster the vertices of a graph into groups of homogeneous connection profiles. In this manuscript, the node groups are assumed to be time invariant to avoid possible identifiability issues. Moreover, the approaches that we propose aim to detect structural changes in the way the node clusters interact with each other. The building block of this thesis is the stochastic block model (SBM), a probabilistic approach initially used in social sciences. The standard SBM assumes that the nodes of a graph belong to hidden (disjoint) clusters and that the probability of observing an edge between two nodes only depends on their clusters. Since no further assumption is made on the connection probabilities, SBM is a very flexible model able to detect different network topologies (hubs, stars, communities, etc.)
Yenerdag, Erdem <1988>. "Contagion Analysis in European Financial Markets Through the Lens of Weighted Stochastic Block Model: Systematically Important Communities of Financial Institutions". Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8816.
Pełny tekst źródłaAlbertyn, Martin. "Generic simulation modelling of stochastic continuous systems". Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05242005-112442.
Pełny tekst źródłaAlkadri, Mohamed Yaser. "Freeway Control Via Ramp Metering: Development of a Basic Building Block for an On-Ramp, Discrete, Stochastic, Mesoscopic, Simulation Model within a Contextual Systems Approach". PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/1308.
Pełny tekst źródłaTabouy, Timothée. "Impact de l’échantillonnage sur l’inférence de structures dans les réseaux : application aux réseaux d’échanges de graines et à l’écologie". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS289/document.
Pełny tekst źródłaIn this thesis we are interested in studying the stochastic block model (SBM) in the presence of missing data. We propose a classification of missing data into two categories Missing At Random and Not Missing At Random for latent variable models according to the model described by D. Rubin. In addition, we have focused on describing several network sampling strategies and their distributions. The inference of SBMs with missing data is made through an adaptation of the EM algorithm : the EM with variational approximation. The identifiability of several of the SBM models with missing data has been demonstrated as well as the consistency and asymptotic normality of the maximum likelihood estimators and variational approximation estimators in the case where each dyad (pair of nodes) is sampled independently and with equal probability. We also looked at SBMs with covariates, their inference in the presence of missing data and how to proceed when covariates are not available to conduct the inference. Finally, all our methods were implemented in an R package available on the CRAN. A complete documentation on the use of this package has been written in addition
Arastuie, Makan. "Generative Models of Link Formation and Community Detection in Continuous-Time Dynamic Networks". University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596718772873086.
Pełny tekst źródłaKsiążki na temat "Bipartite stochastic block model"
Shi, Feng. Learn About Stochastic Block Model in R With Data From Zachary’s Karate Club (1977). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526486097.
Pełny tekst źródłaCoolen, A. C. C., A. Annibale i E. S. Roberts. Graphs on structured spaces. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0010.
Pełny tekst źródłaCzęści książek na temat "Bipartite stochastic block model"
Duvivier, Louis, Rémy Cazabet i Céline Robardet. "Edge Based Stochastic Block Model Statistical Inference". W Complex Networks & Their Applications IX, 462–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65351-4_37.
Pełny tekst źródłaAgarwal, Naman, Afonso S. Bandeira, Konstantinos Koiliaris i Alexandra Kolla. "Multisection in the Stochastic Block Model Using Semidefinite Programming". W Compressed Sensing and its Applications, 125–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69802-1_4.
Pełny tekst źródłaBanerjee, Sayan, Prabhanka Deka i Mariana Olvera-Cravioto. "PageRank Nibble on the Sparse Directed Stochastic Block Model". W Lecture Notes in Computer Science, 147–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32296-9_10.
Pełny tekst źródłaSegovia-Hernández, Juan Gabriel, i Fernando Israel Gómez-Castro. "Using External User-Defined Block Model in Aspen Plus®*". W Stochastic Process Optimization using Aspen Plus®, 125–39. Boca Raton : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155739-7.
Pełny tekst źródłaGhidini, Valentina, Sirio Legramanti i Raffaele Argiento. "Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks". W Springer Proceedings in Mathematics & Statistics, 47–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42413-7_5.
Pełny tekst źródłaAngiulli, Fabrizio, Fabio Fassetti i Cristina Serrao. "A Stochastic Block Model Based Approach to Detect Outliers in Networks". W Lecture Notes in Computer Science, 149–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86472-9_14.
Pełny tekst źródłaBowllan, John, Kailey Cozart, Seyed Mohammad Mahdi Seyednezhad, Anthony Smith i Ronaldo Menezes. "Short Text Tagging Using Nested Stochastic Block Model: A Yelp Case Study". W Complex Networks and Their Applications VIII, 822–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36687-2_68.
Pełny tekst źródłaLiu, Chaochao, Wenjun Wang, Carlo Vittorio Cannistraci, Di Jin i Yueheng Sun. "Layer Clustering-Enhanced Stochastic Block Model for Community Detection in Multiplex Networks". W Advances in Intelligent Systems and Computing, 287–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14680-1_32.
Pełny tekst źródłaWu, Xunxun, Pengfei Jiao, Yaping Wang, Tianpeng Li, Wenjun Wang i Bo Wang. "Dynamic Stochastic Block Model with Scale-Free Characteristic for Temporal Complex Networks". W Database Systems for Advanced Applications, 502–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18579-4_30.
Pełny tekst źródłaWang, Xiaojuan, Pengwei Hu i Lun Hu. "A Novel Stochastic Block Model for Network-Based Prediction of Protein-Protein Interactions". W Intelligent Computing Theories and Application, 621–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60802-6_54.
Pełny tekst źródłaStreszczenia konferencji na temat "Bipartite stochastic block model"
Xu, Xiao, Qing Zhao i Ananthram Swami. "Learning Ordinal Information Under Bipartite Stochastic Block Models". W MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599804.
Pełny tekst źródłaHe, Tiantian, Lu Bai i Yew-Soon Ong. "Manifold Regularized Stochastic Block Model". W 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00115.
Pełny tekst źródłaZhang, Yan, Qixia Jiang i Maosong Sun. "Particle Mixed Membership Stochastic Block Model". W 2012 Eighth International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2012. http://dx.doi.org/10.1109/skg.2012.39.
Pełny tekst źródłaLelarge, Marc, Laurent Massoulie i Jiaming Xu. "Reconstruction in the labeled stochastic block model". W 2013 IEEE Information Theory Workshop (ITW 2013). IEEE, 2013. http://dx.doi.org/10.1109/itw.2013.6691264.
Pełny tekst źródłaYan, Xiaoran. "Bayesian model selection of stochastic block models". W 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752253.
Pełny tekst źródłaCharles, Zachary, i Dimitris Papailiopoulos. "Gradient Coding Using the Stochastic Block Model". W 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437887.
Pełny tekst źródłaPoux-medard, Gael, Julien Velcin i Sabine Loudcher. "Serialized Interacting Mixed Membership Stochastic Block Model". W 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00145.
Pełny tekst źródłaPal, Soumyasundar, i Mark Coates. "Scalable MCMC in Degree Corrected Stochastic Block Model". W ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683631.
Pełny tekst źródłaXu, Jiasheng, Luoyi Fu, Xiaoying Gan i Bo Zhu. "Distributed Community Detection on Overlapping Stochastic Block Model". W 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020. http://dx.doi.org/10.1109/wcsp49889.2020.9299836.
Pełny tekst źródłaCaltagirone, Francesco, Marc Lelarge i Leo Miolane. "Recovering asymmetric communities in the stochastic block model". W 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2016. http://dx.doi.org/10.1109/allerton.2016.7852204.
Pełny tekst źródłaRaporty organizacyjne na temat "Bipartite stochastic block model"
Yue, Dick K., i Yuming Liu. Deterministic Modeling of Water Entry and Drop of An Arbitrary Three-Dimensional Body - A Building Block for Stochastic Model Development. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2001. http://dx.doi.org/10.21236/ada626995.
Pełny tekst źródłaAlkadri, Mohamed. Freeway Control Via Ramp Metering: Development of a Basic Building Block for an On-Ramp, Discrete, Stochastic, Mesoscopic, Simulation Model within a Contextual Systems Approach. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.1307.
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