3,681 Hits in 4.7 sec

Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks [article]

Mostafa Haghir Chehreghani and Talel Abdessalem and and Albert Bifet
2017 arXiv   pre-print
In this paper, first given a network G and a vertex r ∈ V(G), we propose a Metropolis-Hastings MCMC algorithm that samples from the space V(G) and estimates betweenness score of r.  ...  Then, given a network G and a set R ⊂ V(G), we present a Metropolis-Hastings MCMC sampler that samples from the joint space R and V(G) and estimates relative betweenness scores of the vertices in R.  ...  They used the algorithm of [30] as the building block. Hayashi et. al. [20] proposed a fully dynamic algorithm for estimating betweenness centrality of all vertices in a large dynamic network.  ... 
arXiv:1704.07351v3 fatcat:hydhecvhdnhttkp3bmofalewxq

Distributed Aggregate Function Estimation by Biphasically Configured Metropolis-Hasting Weight Model

M. Kenyeres, J. Kenyeres, V. Skorpil, R. Burget
2017 Radioengineering  
In this paper, we introduce an optimized weight model for the average consensus algorithm.  ...  An energy-efficient estimation of an aggregate function can significantly optimize a global event detection or monitoring in wireless sensor networks.  ...  For research, infrastructure of the SIX Center was used.  ... 
doi:10.13164/re.2017.0479 fatcat:ggy6scgiabhuzjwwbe7kxfdaam

Distinct value estimation on peer-to-peer networks

Zubin Joseph, Gautam Das, Leonidas Fegaras
2008 Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments - PETRA '08  
In this paper, we present a technique to obtain estimations of the number of distinct values matching a query on the network.  ...  Peer-to-Peer networks have become very popular on the Internet, with millions of peers all over the world sharing large volumes of data.  ...  The authors in [33] also suggest possible modifications to the Metropolis-Hastings algorithm for attaining required node sampling distributions.  ... 
doi:10.1145/1389586.1389617 dblp:conf/petra/JosephDF08 fatcat:ybzpdbh425dkrkrvyh2m5qwszi

A graph exploration method for identifying influential spreaders in complex networks

Nikos Salamanos, Elli Voudigari, Emmanuel J. Yannakoudakis
2017 Applied Network Science  
same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures.  ...  The proposed method is based on a version of Rank Degree graph sampling algorithm.  ...  Acknowledgements We wish to thank Kyriaki Chryssaki for her valuable support throughout this research.  ... 
doi:10.1007/s41109-017-0047-y pmid:30443581 pmcid:PMC6214278 fatcat:gqopsmwzkvhsxm2uituc4tgcky

Towards a Standard Sampling Methodology on Online Social Networks: Collecting Global Trends on Twitter [article]

C. A. Piña-García, Dongbing Gu
2015 arXiv   pre-print
These random generators will be combined with a Metropolis-Hastings Random Walk (MHRW) in order to improve the sampling process.  ...  One of the most significant current challenges in large-scale online social networks, is to establish a concise and coherent method able to collect and summarize data.  ...  The Metropolis-Hastings Algorithm Metropolis-Hastings algorithms are a major area of interest within the field of Markov chain Monte Carlo (MCMC) methods.  ... 
arXiv:1507.01489v1 fatcat:rogvyzuvijfobpug7jegnuffee

Flow-based generative models for Markov chain Monte Carlo in lattice field theory [article]

M. S. Albergo, G. Kanwar, P. E. Shanahan
2019 arXiv   pre-print
The algorithm is compared with HMC and local Metropolis sampling for ϕ^4 theory in two dimensions.  ...  A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories.  ...  Autocorrelation time for generative Metropolis-Hastings For any Markov chain constructed via a generative Metropolis-Hastings algorithm (with independent update proposals), an observable-independent estimator  ... 
arXiv:1904.12072v2 fatcat:suleq7dmcbbuzb3sktud26gt4i

Optimal Channel Choice for Collaborative Ad-Hoc Dissemination

Liang Hu, Jean-Yves Le Boudec, Milan Vojnoviae
2010 2010 Proceedings IEEE INFOCOM  
Furthermore, we show that the optimum social welfare can be approximated by a decentralized algorithm based on Metropolis-Hastings sampling and give a variant that also accounts for the battery energy.  ...  Collaborative ad-hoc dissemination has been proposed as an efficient means to disseminate information among devices in wireless ad-hoc networks.  ...  Metropolis-Hastings We propose to use the Metropolis-Hastings sampling [11] as it lends itself to distributed optimization, and was successfully used in distributed control problems in wireless networks  ... 
doi:10.1109/infcom.2010.5462163 dblp:conf/infocom/HuBV10 fatcat:3s255kdg5zfw7ha3uf6bblcx2y

A consensus-based decentralized emforamixture of factor analyzers

Gene T. Whipps, Emre Ertin, Randolph L. Moses
2014 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  
Sensor nodes observe an object from different aspects and then, in an unsupervised and distributed manner, learn a joint statistical model for the data manifold.  ...  We derive a consensus-based decentralized expectation maximization (EM) algorithm for learning the parameters of the mixture densities and mixing probabilities.  ...  For the Metropolis-Hastings-based consensus matrix W and the network graph in Fig. 1 , the resulting convergence rate is ρ(W − 1 M 11 T ) = 0.8345.  ... 
doi:10.1109/mlsp.2014.6958933 dblp:conf/mlsp/WhippsEM14 fatcat:nwea4h67xnbe7i2uxyy3p6tuzm

Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution

Lampros Bouranis, Nial Friel, Florian Maire
2017 Social Networks  
However, Bayesian parameter estimation for these models is extremely challenging, since evaluation of the posterior distribution typically involves the calculation of an intractable normalizing constant  ...  Exponential random graph models are an important tool in the statistical analysis of data.  ...  Acknowledgments The authors are grateful for the constructive feedback they received from the Editors and Reviewers at Social Networks.  ... 
doi:10.1016/j.socnet.2017.03.013 fatcat:3gsqa7dxzjg45hwmwernjndaq4

A new learning automata-based sampling algorithm for social networks

Alireza Rezvanian, Mohammad Reza Meybodi
2015 International Journal of Communication Systems  
Due to the large data and privacy issues of social network services, there is only a limited local access to whole network data in a reasonable amount of time.  ...  Experimental results further show that the eDLA based sampling algorithm in comparison with the DLA based sampling algorithm has a 26.93% improvement for the average of KS value for degree distribution  ...  Random jump strategy into Metropolis-Hasting Random Walk is introduced in order to avoid being trapped in local structures by Jin et al. for unbiased estimation [32] .  ... 
doi:10.1002/dac.3091 fatcat:d2nt4atpffhgpp5hh3jrtg67um

A symmetric adaptive algorithm for speeding-up consensus

Daniel Thai, Elizabeth Bodine-Baron, Babak Hassibi
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
, and significantly better than that of the non-adaptive Metropolis-Hastings algorithm. Most importantly, our symmetric adaptive algorithm converges to the sample mean, whereas the method of [?]  ...  In this paper, we present a symmetric adaptive weight algorithm for distributed consensus averaging on bi-directional noiseless networks.  ...  , nor as simple as the Metropolis-Hastings method, fills a niche in between, allowing convergence to the mean faster than the Metropolis-Hastings method, while still requiring only local information.  ... 
doi:10.1109/icassp.2010.5496237 dblp:conf/icassp/ThaiBH10 fatcat:tnku5kjmtrcevld5hpcmrvqx3e

Applicability of Generalized Metropolis-Hastings Algorithm to Estimating Aggregate Functions in Wireless Sensor Networks

Martin Kenyeres, Jozef Kenyeres
2020 Advances in Science, Technology and Engineering Systems  
In this paper, we provide an analysis of the generalized Metropolis-Hastings algorithm for data aggregation with a fully-distributed stopping criterion.  ...  In this paper, we analyze and compare the performance of the mentioned algorithm with various mixing parameters for distributed averaging, for distributed summing, and for distributed graph order estimation  ...  regular graph for four values of mixing parameter 1.3 Generalized Metropolis-Hastings for Data Aggregation In this paper, we focus our attention on MH, which was proposed by Nicolas Metropolis et al  ... 
doi:10.25046/aj050528 fatcat:uxb26mxs7baqnjli7rpt6i3drq

Sampling social networks using shortest paths

Alireza Rezvanian, Mohammad Reza Meybodi
2015 Physica A: Statistical Mechanics and its Applications  
algorithm is compared with other well-known sampling methods in terms of RE, NMSE, and KS test. • The experimental results show that proposed sampling method is a proper method for sampling social networks  ...  In this paper, we propose to use the concept of shortest path for sampling social networks.  ...  The measure of centrality can be chosen to be any measure of centrality such as betweenness or degree. For this experiment we consider the measure of centrality to be degree centrality. IV.  ... 
doi:10.1016/j.physa.2015.01.030 fatcat:myd2wjt7vnedzk2piicmathrzy

Bayesian Nonparametric Mixtures of Exponential Random Graph Models for Ensembles of Networks [article]

Sa Ren, Xue Wang, Peng Liu, Jian Zhang
2022 arXiv   pre-print
Ensembles of networks arise in various fields where multiple independent networks are observed on the same set of nodes, for example, a collection of brain networks constructed on the same brain regions  ...  Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we employ the intermediate importance sampling technique inside the Metropolis-within-slice sampling scheme, which addressed the problem  ...  MCMH algorithm (Liang and Jin, 2013) samples from the posterior ERGMs by using an importance sampling estimator to approximate k(θ)/k(θ ) in the Metropolis Hastings algorithm.  ... 
arXiv:2201.08153v1 fatcat:hnjecq6ygrg4pnija5uvyf7mae

MCMC estimation for the p2 network regression model with crossed random effects

Bonne J. H. Zijlstra, Marijtje A. J. Duijn, Tom A. B. Snijders
2009 British Journal of Mathematical & Statistical Psychology  
The availability of the MCMC estimation algorithms for the p 2 model provides a large improvement, resulting in largely unbiased estimates with good coverage rates.  ...  these variables are obtained by applying the Metropolis-Hastings algorithm.  ... 
doi:10.1348/000711007x255336 pmid:19208289 fatcat:ouyhpntgevgnpnljlyupebv3eq
« Previous Showing results 1 — 15 out of 3,681 results