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Статті в журналах з теми "Stochastic block models"
Abbe, Emmanuel. "Community Detection and Stochastic Block Models." Foundations and Trends® in Communications and Information Theory 14, no. 1-2 (2018): 1–162. http://dx.doi.org/10.1561/0100000067.
Повний текст джерелаVo, 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.
Повний текст джерелаHan, 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.
Повний текст джерелаYang, Guangren, Songshan Yang, and Wang Zhou. "Adjacency matrix comparison for stochastic block models." Random Matrices: Theory and Applications 08, no. 03 (July 2019): 1950010. http://dx.doi.org/10.1142/s2010326319500102.
Повний текст джерелаLatouche, 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.
Повний текст джерелаGhosh, Prasenjit, Debdeep Pati, and Anirban Bhattacharya. "Posterior Contraction Rates for Stochastic Block Models." Sankhya A 82, no. 2 (October 14, 2019): 448–76. http://dx.doi.org/10.1007/s13171-019-00180-5.
Повний текст джерелаCarlen, Jane, Jaume de Dios Pont, Cassidy Mentus, Shyr-Shea Chang, Stephanie Wang, and Mason A. Porter. "Role detection in bicycle-sharing networks using multilayer stochastic block models." Network Science 10, no. 1 (March 2022): 46–81. http://dx.doi.org/10.1017/nws.2021.21.
Повний текст джерелаMcMillan, Audra, and Adam Smith. "When is non-trivial estimation possible for graphons and stochastic block models?‡." Information and Inference: A Journal of the IMA 7, no. 2 (August 23, 2017): 169–81. http://dx.doi.org/10.1093/imaiai/iax010.
Повний текст джерелаKloumann, Isabel M., Johan Ugander, and Jon Kleinberg. "Block models and personalized PageRank." Proceedings of the National Academy of Sciences 114, no. 1 (December 20, 2016): 33–38. http://dx.doi.org/10.1073/pnas.1611275114.
Повний текст джерелаCoulson, 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.
Повний текст джерелаДисертації з теми "Stochastic block models"
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/.
Повний текст джерелаCorneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.
Повний текст джерелаThis 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.)
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.
Повний текст джерелаEl 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.
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.
Повний текст джерелаJunuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
Повний текст джерелаGulikers, Lennart. "Sur deux problèmes d’apprentissage automatique : la détection de communautés et l’appariement adaptatif." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE062/document.
Повний текст джерелаIn this thesis, we study two problems of machine learning: (I) community detection and (II) adaptive matching. I) It is well-known that many networks exhibit a community structure. Finding those communities helps us understand and exploit general networks. In this thesis we focus on community detection using so-called spectral methods based on the eigenvectors of carefully chosen matrices. We analyse their performance on artificially generated benchmark graphs. Instead of the classical Stochastic Block Model (which does not allow for much degree-heterogeneity), we consider a Degree-Corrected Stochastic Block Model (DC-SBM) with weighted vertices, that is able to generate a wide class of degree sequences. We consider this model in both a dense and sparse regime. In the dense regime, we show that an algorithm based on a suitably normalized adjacency matrix correctly classifies all but a vanishing fraction of the nodes. In the sparse regime, we show that the availability of only a small amount of information entails the existence of an information-theoretic threshold below which no algorithm performs better than random guess. On the positive side, we show that an algorithm based on the non-backtracking matrix works all the way down to the detectability threshold in the sparse regime, showing the robustness of the algorithm. This follows after a precise characterization of the non-backtracking spectrum of sparse DC-SBM's. We further perform tests on well-known real networks. II) Online two-sided matching markets such as Q&A forums and online labour platforms critically rely on the ability to propose adequate matches based on imperfect knowledge of the two parties to be matched. We develop a model of a task / server matching system for (efficient) platform operation in the presence of such uncertainty. For this model, we give a necessary and sufficient condition for an incoming stream of tasks to be manageable by the system. We further identify a so-called back-pressure policy under which the throughput that the system can handle is optimized. We show that this policy achieves strictly larger throughput than a natural greedy policy. Finally, we validate our model and confirm our theoretical findings with experiments based on user-contributed content on an online platform
Kasinos, Stavros. "Seismic response analysis of linear and nonlinear secondary structures." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/33728.
Повний текст джерелаLudkin, Matthew Robert. "The autoregressive stochastic block model with changes in structure." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/125642/.
Повний текст джерелаŠigut, Jiří. "Modely oceňování opcí se stochastickou volatilitou." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150113.
Повний текст джерелаBartoň, Ľuboš. "Oceňovanie opcií so stochastickou volatilitou." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-77823.
Повний текст джерелаКниги з теми "Stochastic block models"
1951-, Levendorskiĭ Serge, ed. Non-Gaussian Merton-Black-Scholes theory. Singapore: World Scientific, 2002.
Знайти повний текст джерелаShi, 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.
Повний текст джерелаGeorge, Christakos, ed. Interdisciplinary public health reasoning and epidemic modelling: The case of Black Death. Berlin: Springer, 2005.
Знайти повний текст джерелаAbbe, Emmanuel. Community Detection and Stochastic Block Models. Now Publishers, 2018.
Знайти повний текст джерелаCoolen, 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.
Повний текст джерелаBack, Kerry E. Forwards, Futures, and More Option Pricing. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0017.
Повний текст джерелаMaggiore, Michele. Gravitational Waves. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198570899.001.0001.
Повний текст джерелаTuite, Cl´ıodhna, Michael O’Neill, and Anthony Brabazon. Economic and Financial Modeling with Genetic Programming. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.10.
Повний текст джерелаChristakos, George, Ricardo A. Olea, Marc L. Serre, Hwa-Lung Yu, and Lin-Lin Wang. Interdisciplinary Public Health Reasoning and Epidemic Modelling: The Case of Black Death. Springer, 2005.
Знайти повний текст джерелаChristakos, George, Ricardo A. Olea, Marc L. Serre, Hwa-Lung Yu, and Lin-Lin Wang. Interdisciplinary Public Health Reasoning and Epidemic Modelling: The Case of Black Death. Springer, 2010.
Знайти повний текст джерелаЧастини книг з теми "Stochastic block models"
Won, Chee Sun, and Robert M. Gray. "Block-Wise Markov Models." In Stochastic Image Processing, 125–48. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-8857-7_5.
Повний текст джерелаLi, Quan-Lin. "Block-Structured Markov Chains." In Constructive Computation in Stochastic Models with Applications, 72–130. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11492-2_2.
Повний текст джерелаLi, Quan-Lin. "Block-Structured Markov Renewal Processes." In Constructive Computation in Stochastic Models with Applications, 288–330. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11492-2_6.
Повний текст джерелаDuvivier, Louis, Céline Robardet, and Rémy Cazabet. "Minimum Entropy Stochastic Block Models Neglect Edge Distribution Heterogeneity." In Complex Networks and Their Applications VIII, 545–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36687-2_45.
Повний текст джерелаSun, Xiaokun, Jieru Yang, Junya Yao, Qian Sun, Yong Su, Hengpeng Xu, and Jun Wang. "Financial Risk Prediction Based on Stochastic Block and Cox Proportional Hazards Models." In Lecture Notes in Electrical Engineering, 223–31. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0386-1_28.
Повний текст джерелаSun, Xiaokun, Jieru Yang, Junya Yao, Qian Sun, Yong Su, Hengpeng Xu, and Jun Wang. "Financial Risk Prediction Based on Stochastic Block and Cox Proportional Hazards Models." In Lecture Notes in Electrical Engineering, 548–56. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0390-8_67.
Повний текст джерелаDuvivier, 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.
Повний текст джерелаAgarwal, 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.
Повний текст джерелаSegovia-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.
Повний текст джерелаSandmann, Klaus. "Grundlagen zeitstetiger Kursprozesse und das Black-Scholes-Modell." In Einführung in die Stochastik der Finanzmärkte, 283–346. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03301-8_7.
Повний текст джерелаТези доповідей конференцій з теми "Stochastic block models"
Gribel, Daniel, Thibaut Vidal, and Michel Gendreau. "Assortative-Constrained Stochastic Block Models." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413275.
Повний текст джерелаYan, 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.
Повний текст джерелаRobinson, Jace, and Derek Doran. "Seasonality in dynamic stochastic block models." In WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3109424.
Повний текст джерелаBhatt, Alankrita, Ziao Wang, Chi Wang, and Lele Wang. "Universal Graph Compression: Stochastic Block Models." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517737.
Повний текст джерелаDing, Jingqiu, Tommaso D'Orsi, Rajai Nasser, and David Steurer. "Robust recovery for stochastic block models." In 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2022. http://dx.doi.org/10.1109/focs52979.2021.00046.
Повний текст джерелаJog, Varun, and Po-Ling Loh. "Recovering communities in weighted stochastic block models." In 2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton). IEEE, 2015. http://dx.doi.org/10.1109/allerton.2015.7447159.
Повний текст джерелаShafiei, Mahdi, and Hugh Chipman. "Mixed-Membership Stochastic Block-Models for Transactional Networks." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.88.
Повний текст джерелаXu, 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.
Повний текст джерелаMathews, Heather, Vaishakhi Mayya, Alexander Volfovsky, and Galen Reeves. "Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022612.
Повний текст джерелаGadde, Akshay, Eyal En Gad, Salman Avestimehr, and Antonio Ortega. "Active learning for community detection in stochastic block models." In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541627.
Повний текст джерелаЗвіти організацій з теми "Stochastic block models"
Menzio, Guido, and Shouyong Shi. Block Recursive Equilibria for Stochastic Models of Search on the Job. Cambridge, MA: National Bureau of Economic Research, April 2009. http://dx.doi.org/10.3386/w14907.
Повний текст джерелаYue, 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.
Повний текст джерелаAlkadri, 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.
Повний текст джерела