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Опубліковано: 1 червня 2024

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Статті в журналах з теми "Bipartite stochastic block model":

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.
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Статті в журналах Дисертації

Дисертації з теми "Bipartite stochastic block model":

1

Sigalla, Suzanne. "Contributions to structured high-dimensional inference." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG013.

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Dans cette thèse, nous considérons les trois problèmes suivants : le problème de clustering dans le Bipartite Stochastic Block Model, le problème de classification de documents dans le cadre des topic models, et le problème de benign overfitting dans le cadre de régression non paramétrique. Tout d'abord, nous considérons le problème de clustering dans le Bipartite Stochastic Block Model (BSBM). Le BSBM est une généralisation non symétrique du Stochastic Block Model, avec deux ensembles de sommets. Nous introduisons un algorithme appelé le Hollowed Lloyd's algorithm, qui permet de classer les sommets du plus petit ensemble avec grande probabilité. Nous fournissons des garanties statistiques sur cet algorithme, qui est rapide et simple à implémenter. Nous établissons une condition suffisante pour le clustering dans le BSBM. Nos résultats améliorent les travaux précédents sur le BSBM, en particulier dans le cadre de grande dimension. Deuxièmement, nous étudions le problème de la classification de documents dans le cadre des topic models. Les topic models permettent d'exploiter des structures sous-jacentes dans un grand corpus de documents et ainsi de réduire la dimension du problème considéré. Chaque topic est vu comme une distribution de probabilité sur le dictionnaire de mots du corpus, et chaque document est vu comme un mélange de topics. Nous introduisons un algorithme appelé Successive Projection Overlapping Clustering (SPOC), inspiré du Successive Projection Algorithm pour le problème de Nonnegative Matrix Factorization. L'algorithme SPOC est rapide et simple à implémenter. Nous fournissons des garanties statistiques sur le résultat de l'algorithme SPOC. En particulier, nous fournissons des bornes minimax inférieures et supérieures sur son risque d'estimation pour les normes de Frobenius et l1, bornes correspondant à de faibles facteurs près. Notre procédure de clustering est adaptative en le nombre de topics. Enfin, le troisième problème étudié lors de cette thèse porte sur la régression non paramétrique. Nous considérons des estimateurs par polynômes locaux avec des noyaux singuliers. Nous prouvons que ces estimateurs sont minimax optimaux, adaptatifs en la régularité et interpolants avec une probabilité élevée. Cette propriété est appelée benign overfitting
In 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
2

Ludkin, Matthew Robert. "The autoregressive stochastic block model with changes in structure." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/125642/.

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Network science has been a growing subject for the last three decades, with sta- tistical analysis of networks seing an explosion since the advent of online social networks. An important model within network analysis is the stochastic block model, which aims to partition the set of nodes of a network into groups which behave in a similar way. This thesis proposes Bayesian inference methods for problems related to the stochastic block model for network data. The presented research is formed of three parts. Firstly, two Markov chain Monte Carlo samplers are proposed to sample from the posterior distribution of the number of blocks, block memberships and edge-state parameters in the stochastic block model. These allow for non-binary and non-conjugate edge models, something not considered in the literature. Secondly, a dynamic extension to the stochastic block model is presented which includes autoregressive terms. This novel approach to dynamic network models allows the present state of an edge to influence future states, and is therefore named the autoregresssive stochastic block model. Furthermore, an algorithm to perform inference on changes in block membership is given. This problem has gained some attention in the literature, but not with autoregressive features to the edge-state distribution as presented in this thesis. Thirdly, an online procedure to detect changes in block membership in the au- toregresssive stochastic block model is presented. This allows networks to be monitored through time, drastically reducing the data storage requirements. On top of this, the network parameters can be estimated together with the block memberships. Finally, conclusions are drawn from the above contributions in the context of the network analysis literature and future directions for research are identified.
3

Paltrinieri, Federico. "Modeling temporal networks with dynamic stochastic block models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18805/.

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Osservando il recente interesse per le reti dinamiche temporali e l'ampio numero di campi di applicazione, questa tesi ha due principali propositi: primo, di analizzare alcuni modelli teorici di reti temporali, specialmente lo stochastic blockmodel dinamico, al fine di descrivere la dinamica di sistemi reali e fare previsioni. Il secondo proposito della tesi è quello di creare due nuovi modelli teorici, basati sulla teoria dei processi autoregressivi, dai quali inferire nuovi parametri dalle reti temporali, come la matrice di evoluzione di stato e una migliore stima della varianza del rumore del processo di evoluzione temporale. Infine, tutti i modelli sono testati su un data set interbancario: questi rivelano la presenza di un evento atteso che divide la rete temporale in due periodi distinti con differenti configurazioni e parametri.
4

Vallès, 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.

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L'estudi de xarxes ha contribuït a la comprensió de sistemes complexos en una àmplia gamma de camps com la biologia molecular i cel·lular, l'anatomia, la neurociència, l'ecologia, l'economia i la sociologia. No obstant, el coneixement disponible sobre molts sistemes reals encara és limitat, per aquesta raó el poder predictiu de la ciència en xarxes s'ha de millorar per disminuir la bretxa entre coneixement i informació. Per abordar aquest tema fem servir la família de 'Stochastic Block Models' (SBM), una família de models generatius que està guanyant gran interès recentment a causa de la seva adaptabilitat a qualsevol tipus de xarxa. L'objectiu d'aquesta tesi és el desenvolupament de noves metodologies d'inferència basades en SBM que perfeccionaran la nostra comprensió de les xarxes complexes. En primer lloc, investiguem en quina mesura fer un mostreg sobre models pot millorar significativament la capacitat de predicció que considerar un únic conjunt òptim de paràmetres. Un cop sabem quin model és capaç de descriure millor una xarxa determinada, apliquem aquest mètode en un cas particular d'una xarxa real: una xarxa basada en les interaccions/sutures entre els ossos del crani en nounats. Concretament, descobrim que les sutures tancades a causa d'una malaltia patològica en el nounat humà son menys probables, des d'un punt de vista morfològic, que les sutures tancades sota un desenvolupament normal. Recents investigacions en xarxes multicapa conclou que el comportament de les xarxes d'una sola capa són diferents de les de múltiples capes; d'altra banda, les xarxes del món real se'ns presenten com xarxes d'una sola capa.
El 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.
5

Corneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.

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Cette thèse porte sur l’analyse de graphes dynamiques, définis en temps discret ou continu. Nous introduisons une nouvelle extension dynamique du modèle a blocs stochastiques (SBM), appelée dSBM, qui utilise des processus de Poisson non homogènes pour modéliser les interactions parmi les paires de nœuds d’un graphe dynamique. Les fonctions d’intensité des processus ne dépendent que des classes des nœuds comme dans SBM. De plus, ces fonctions d’intensité ont des propriétés de régularité sur des intervalles temporels qui sont à estimer, et à l’intérieur desquels les processus de Poisson redeviennent homogènes. Un récent algorithme d’estimation pour SBM, qui repose sur la maximisation d’un critère exact (ICL exacte) est ici adopté pour estimer les paramètres de dSBM et sélectionner simultanément le modèle optimal. Ensuite, un algorithme exact pour la détection de rupture dans les séries temporelles, la méthode «pruned exact linear time» (PELT), est étendu pour faire de la détection de rupture dans des données de graphe dynamique selon le modèle dSBM. Enfin, le modèle dSBM est étendu ultérieurement pour faire de l’analyse de réseau textuel dynamique. Les réseaux sociaux sont un exemple de réseaux textuels: les acteurs s’échangent des documents (posts, tweets, etc.) dont le contenu textuel peut être utilisé pour faire de la classification et détecter la structure temporelle du graphe dynamique. Le modèle que nous introduisons est appelé «dynamic stochastic topic block model» (dSTBM)
This 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.)
6

Yenerdag, Erdem <1988&gt. "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.

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This study provides a new perspective to analyze systemic risk and contagion channels of financial markets by proposing Weighted Stochastic Block Model (WSBM) as a generative model for the financial networks. WSBM allows regulators to analyze systemic risk and contagion channels of financial markets by the topological features of WSBM communities. In the empirical application of the WSBM, it is found that the number of communities tends to increase during the financial crisis which can be analyzed as a new early warning indicator of systemic risk. In addition, a new ranking method, based on the new notion of systematically important communities of financial institutions, is provided to assess the systemically important financial institutions.
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Albertyn, Martin. "Generic simulation modelling of stochastic continuous systems." Thesis, Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-05242005-112442.

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Alkadri, 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.

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One of the most effective measures of congestion control on freeways has been ramp metering, where vehicle entry to the freeway is regulated by traffic signals (meters). Meters are run with calibrated influx rates to prevent highway saturation. However, recent observations of some metering sites in San Diego, CA indicate that metering, during peak hour demand, is helping freeway flow while sometimes creating considerable traffic back-ups on local streets, transferring congestion problems from the freeway to intersections. Metering problems stem largely from the difficulty of designing an integrated, dynamic metering scheme that responds not only to changing freeway conditions but also to fluctuating demand throughout the ramp network; a scheme whose objective is to maintain adequate freeway throughput as well as minimize disproportionate ramp delays and queue overspills onto surface streets. Simulation modeling is a versatile, convenient, relatively inexpensive and safe systems analysis tool for evaluating alternative strategies to achieve the above objective. The objective of this research was to establish a basic building block for a discrete system simulation model, ONRAMP, based on a stochastic, mesoscopic, queueing approach. ONRAMP is for modeling entrance ramp geometry, vehicular generation, platooning and arrivals, queueing activities, meters and metering rates. The architecture of ONRAMP's molecular unit is designed in a fashion so that it can be, with some model calibration, duplicated for a number of ramps and, if necessary, integrated into some other larger freeway network models. SLAM.II simulation language is used for computer implementation. ONRAMP has been developed and partly validated using data from eight ramps at Interstate-B in San Diego. From a systems perspective, simulation will be short-sided and problem analysis is incomplete unless the other non-technical metering problems are explored and considered. These problems include the impacts of signalizing entrance ramps on the vitality of adjacent intersections, land use and development, "fair" geographic distribution of meters and metering rates throughout the freeway corridor, public acceptance and enforcement, and the role and influence of organizations in charge of decision making in this regard. Therefore, an outline of a contextual systems approach for problem analysis is suggested. Benefits and problems of freeway control via ramp metering, both operational short-term and strategic long-term, are discussed in two dimensions: global (freeway) and local (intersection). The results of a pilot study which includes interviews with field experts and law enforcement officials and a small motorist survey are presented.
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Tabouy, 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.

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Dans cette thèse nous nous intéressons à l’étude du modèle à bloc stochastique (SBM) en présence de données manquantes. Nous proposons une classification des données manquantes en deux catégories Missing At Random et Not Missing At Random pour les modèles à variables latentes suivant le modèle décrit par D. Rubin. De plus, nous nous sommes attachés à décrire plusieurs stratégies d’échantillonnages de réseau et leurs lois. L’inférence des modèles de SBM avec données manquantes est faite par l’intermédiaire d’une adaptation de l’algorithme EM : l’EM avec approximation variationnelle. L’identifiabilité de plusieurs des SBM avec données manquantes a pu être démontrée ainsi que la consistance et la normalité asymptotique des estimateurs du maximum de vraisemblance et des estimateurs avec approximation variationnelle dans le cas où chaque dyade (paire de nœuds) est échantillonnée indépendamment et avec même probabilité. Nous nous sommes aussi intéressés aux modèles de SBM avec covariables, à leurs inférence en présence de données manquantes et comment procéder quand les covariables ne sont pas disponibles pour conduire l’inférence. Finalement, toutes nos méthodes ont été implémenté dans un package R disponible sur le CRAN. Une documentation complète sur l’utilisation de ce package a été écrite en complément
In 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
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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.

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Книги з теми "Bipartite stochastic block model":

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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.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Graphs on structured spaces. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0010.

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This chapter moves beyond viewing nodes as homogeneous dots set on a plane. To introduce more complicated underlying space, multiplex networks (which are defined with layers of interaction on the same underlying node set) and temporal (time-dependent) networks are discussed. It shown that despite the much more complicated underlying space, many of the techniques developed in earlier chapters can be applied. Heterogeneous nodes are introduced as an extension of the stochastic block model for community structure, then extended using methods developed in earlier chapters to more general (continuous) node attributes such as fitness. The chapter closes with a discussion of the intersections and similarities between the many alternative models for capturing topological features that have been presented in the book.

Частини книг з теми "Bipartite stochastic block model":

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Duvivier, Louis, Rémy Cazabet, and Céline Robardet. "Edge Based Stochastic Block Model Statistical Inference." In Complex Networks & Their Applications IX, 462–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65351-4_37.

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Agarwal, Naman, Afonso S. Bandeira, Konstantinos Koiliaris, and Alexandra Kolla. "Multisection in the Stochastic Block Model Using Semidefinite Programming." In Compressed Sensing and its Applications, 125–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69802-1_4.

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Banerjee, Sayan, Prabhanka Deka, and Mariana Olvera-Cravioto. "PageRank Nibble on the Sparse Directed Stochastic Block Model." In Lecture Notes in Computer Science, 147–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32296-9_10.

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Segovia-Hernández, Juan Gabriel, and Fernando Israel Gómez-Castro. "Using External User-Defined Block Model in Aspen Plus®*." In 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.

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Ghidini, Valentina, Sirio Legramanti, and Raffaele Argiento. "Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks." In Springer Proceedings in Mathematics & Statistics, 47–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42413-7_5.

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Angiulli, Fabrizio, Fabio Fassetti, and Cristina Serrao. "A Stochastic Block Model Based Approach to Detect Outliers in Networks." In Lecture Notes in Computer Science, 149–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86472-9_14.

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Bowllan, John, Kailey Cozart, Seyed Mohammad Mahdi Seyednezhad, Anthony Smith, and Ronaldo Menezes. "Short Text Tagging Using Nested Stochastic Block Model: A Yelp Case Study." In 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.

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Liu, Chaochao, Wenjun Wang, Carlo Vittorio Cannistraci, Di Jin, and Yueheng Sun. "Layer Clustering-Enhanced Stochastic Block Model for Community Detection in Multiplex Networks." In 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.

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Wu, Xunxun, Pengfei Jiao, Yaping Wang, Tianpeng Li, Wenjun Wang, and Bo Wang. "Dynamic Stochastic Block Model with Scale-Free Characteristic for Temporal Complex Networks." In Database Systems for Advanced Applications, 502–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18579-4_30.

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Wang, Xiaojuan, Pengwei Hu, and Lun Hu. "A Novel Stochastic Block Model for Network-Based Prediction of Protein-Protein Interactions." In Intelligent Computing Theories and Application, 621–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60802-6_54.

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Тези доповідей конференцій з теми "Bipartite stochastic block model":

1

Xu, Xiao, Qing Zhao, and Ananthram Swami. "Learning Ordinal Information Under Bipartite Stochastic Block Models." In MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599804.

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He, Tiantian, Lu Bai, and Yew-Soon Ong. "Manifold Regularized Stochastic Block Model." In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2019. http://dx.doi.org/10.1109/ictai.2019.00115.

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Zhang, Yan, Qixia Jiang, and Maosong Sun. "Particle Mixed Membership Stochastic Block Model." In 2012 Eighth International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2012. http://dx.doi.org/10.1109/skg.2012.39.

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Lelarge, Marc, Laurent Massoulie, and Jiaming Xu. "Reconstruction in the labeled stochastic block model." In 2013 IEEE Information Theory Workshop (ITW 2013). IEEE, 2013. http://dx.doi.org/10.1109/itw.2013.6691264.

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Yan, Xiaoran. "Bayesian model selection of stochastic block models." In 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.

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Charles, Zachary, and Dimitris Papailiopoulos. "Gradient Coding Using the Stochastic Block Model." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437887.

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Poux-medard, Gael, Julien Velcin, and Sabine Loudcher. "Serialized Interacting Mixed Membership Stochastic Block Model." In 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022. http://dx.doi.org/10.1109/icdm54844.2022.00145.

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Pal, Soumyasundar, and Mark Coates. "Scalable MCMC in Degree Corrected Stochastic Block Model." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683631.

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Xu, Jiasheng, Luoyi Fu, Xiaoying Gan, and Bo Zhu. "Distributed Community Detection on Overlapping Stochastic Block Model." In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020. http://dx.doi.org/10.1109/wcsp49889.2020.9299836.

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Caltagirone, Francesco, Marc Lelarge, and Leo Miolane. "Recovering asymmetric communities in the stochastic block model." In 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2016. http://dx.doi.org/10.1109/allerton.2016.7852204.

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Звіти організацій з теми "Bipartite stochastic block model":

1

Yue, Dick K., and 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, August 2001. http://dx.doi.org/10.21236/ada626995.

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Alkadri, 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, January 2000. http://dx.doi.org/10.15760/etd.1307.

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