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Journal articles on the topic 'Stochastic block models'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

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.

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2

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

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The discovery of the “hidden population”, whose size and membership are unknown, is made possible by assuming that its members are connected in a social network by their relationships. We explore these groups by a chain-referral sampling (CRS) method, where participants recommend the people they know. This leads to the study of a Markov chain on a random graph where vertices represent individuals and edges connecting any two nodes describe the relationships between corresponding people. We are interested in the study of CRS process on the stochastic block model (SBM), which extends the well-known Erdös-Rényi graphs to populations partitioned into communities. The SBM considered here is characterized by a number of vertices N, a number of communities (blocks) m, proportion of each community π = (π1, …, πm) and a pattern for connection between blocks P = (λkl∕N)(k,l)∈{1,…,m}2. In this paper, we give a precise description of the dynamic of CRS process in discrete time on an SBM. The difficulty lies in handling the heterogeneity of the graph. We prove that when the population’s size is large, the normalized stochastic process of the referral chain behaves like a deterministic curve which is the unique solution of a system of ODEs.
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3

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

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With the rapid expansion of graphs and networks and the growing magnitude of data from all areas of science, effective treatment and compression schemes of context-dependent data is extremely desirable. A particularly interesting direction is to compress the data while keeping the “structural information” only and ignoring the concrete labelings. Under this direction, Choi and Szpankowski introduced the structures (unlabeled graphs) which allowed them to compute the structural entropy of the Erdős–Rényi random graph model. Moreover, they also provided an asymptotically optimal compression algorithm that (asymptotically) achieves this entropy limit and runs in expectation in linear time. In this paper, we consider the stochastic block models with an arbitrary number of parts. Indeed, we define a partitioned structural entropy for stochastic block models, which generalizes the structural entropy for unlabeled graphs and encodes the partition information as well. We then compute the partitioned structural entropy of the stochastic block models, and provide a compression scheme that asymptotically achieves this entropy limit.
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4

Yang, 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.

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In this paper, we study whether two networks arising from two stochastic block models have the same connection structures by comparing their adjacency matrices. We conduct Monte Carlo simulations study to examine the finite sample performance of the proposed method. A real data example is used to illustrate the proposed methodology.
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5

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

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6

Ghosh, 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.

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7

Carlen, 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.

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AbstractIn urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work blocks, home blocks, and other blocks; they also reveal activity patterns that are specific to each city. Our work gives insights for the design and maintenance of bicycle-sharing systems, and it contributes new methodology for community detection in temporal and multilayer networks with heterogeneous degrees.
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8

McMillan, 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.

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Abstract Block graphons (also called stochastic block models) are an important and widely studied class of models for random networks. We provide a lower bound on the accuracy of estimators for block graphons with a large number of blocks. We show that, given only the number $k$ of blocks and an upper bound $\rho$ on the values (connection probabilities) of the graphon, every estimator incurs error ${\it{\Omega}}\left(\min\left(\rho, \sqrt{\frac{\rho k^2}{n^2}}\right)\right)$ in the $\delta_2$ metric with constant probability for at least some graphons. In particular, our bound rules out any non-trivial estimation (that is, with $\delta_2$ error substantially less than $\rho$) when $k\geq n\sqrt{\rho}$. Combined with previous upper and lower bounds, our results characterize, up to logarithmic terms, the accuracy of graphon estimation in the $\delta_2$ metric. A similar lower bound to ours was obtained independently by Klopp et al.
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9

Kloumann, 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.

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Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the “seed set expansion problem”: given a subsetSof nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of “landing probabilities” of a random walk rooted at the seed set, ranking nodes according to weighted sums of landing probabilities of different length walks. Both schemes, however, lack an a priori relationship to the seed set objective. In this work, we develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the landing probabilities of two classes in a stochastic block model and find, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameterαthat depends on the block model parameters. This connection provides a formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We use this connection to propose more advanced techniques incorporating higher moments of landing probabilities; our advanced methods exhibit greatly improved performance, despite being simple linear classification rules, and are even competitive with belief propagation.
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10

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

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Abstract We use the Stein‒Chen method to obtain compound Poisson approximations for the distribution of the number of subgraphs in a generalised stochastic block model which are isomorphic to some fixed graph. This model generalises the classical stochastic block model to allow for the possibility of multiple edges between vertices. We treat the case that the fixed graph is a simple graph and that it has multiple edges. The former results apply when the fixed graph is a member of the class of strictly balanced graphs and the latter results apply to a suitable generalisation of this class to graphs with multiple edges. We also consider a further generalisation of the model to pseudo-graphs, which may include self-loops as well as multiple edges, and establish a parameter regime in the multiple edge stochastic block model in which Poisson approximations are valid. The results are applied to obtain Poisson and compound Poisson approximations (in different regimes) for subgraph counts in the Poisson stochastic block model and degree corrected stochastic block model of Karrer and Newman (2011).
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11

Marino, Maria Francesca, and Silvia Pandolfi. "Hybrid maximum likelihood inference for stochastic block models." Computational Statistics & Data Analysis 171 (July 2022): 107449. http://dx.doi.org/10.1016/j.csda.2022.107449.

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12

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

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13

Bartolucci, Francesco, Maria Francesca Marino, and Silvia Pandolfi. "Dealing with reciprocity in dynamic stochastic block models." Computational Statistics & Data Analysis 123 (July 2018): 86–100. http://dx.doi.org/10.1016/j.csda.2018.01.010.

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14

Lei, Jing, and Alessandro Rinaldo. "Consistency of spectral clustering in stochastic block models." Annals of Statistics 43, no. 1 (February 2015): 215–37. http://dx.doi.org/10.1214/14-aos1274.

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15

Hu, Jianwei, Hong Qin, Ting Yan, and Yunpeng Zhao. "Corrected Bayesian Information Criterion for Stochastic Block Models." Journal of the American Statistical Association 115, no. 532 (August 14, 2019): 1771–83. http://dx.doi.org/10.1080/01621459.2019.1637744.

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16

Boyd, Zachary M., Mason A. Porter, and Andrea L. Bertozzi. "Stochastic Block Models are a Discrete Surface Tension." Journal of Nonlinear Science 30, no. 5 (April 22, 2019): 2429–62. http://dx.doi.org/10.1007/s00332-019-09541-8.

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17

Baek, Mina, and Choongrak Kim. "A review on spectral clustering and stochastic block models." Journal of the Korean Statistical Society 50, no. 3 (March 10, 2021): 818–31. http://dx.doi.org/10.1007/s42952-021-00112-w.

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18

Su, Liangjun, Wuyi Wang, and Yichong Zhang. "Strong Consistency of Spectral Clustering for Stochastic Block Models." IEEE Transactions on Information Theory 66, no. 1 (January 2020): 324–38. http://dx.doi.org/10.1109/tit.2019.2934157.

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19

Stegehuis, Clara, and Laurent Massoulie. "Efficient Inference in Stochastic Block Models With Vertex Labels." IEEE Transactions on Network Science and Engineering 7, no. 3 (July 1, 2020): 1215–25. http://dx.doi.org/10.1109/tnse.2019.2913949.

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20

Chen, Yudong, Xiaodong Li, and Jiaming Xu. "Convexified modularity maximization for degree-corrected stochastic block models." Annals of Statistics 46, no. 4 (August 2018): 1573–602. http://dx.doi.org/10.1214/17-aos1595.

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21

Zhang, Xue, Xiaojie Wang, Chengli Zhao, Dongyun Yi, and Zheng Xie. "Degree-corrected stochastic block models and reliability in networks." Physica A: Statistical Mechanics and its Applications 393 (January 2014): 553–59. http://dx.doi.org/10.1016/j.physa.2013.08.061.

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22

Lei, Jing. "A goodness-of-fit test for stochastic block models." Annals of Statistics 44, no. 1 (February 2016): 401–24. http://dx.doi.org/10.1214/15-aos1370.

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23

Zhang, Anderson Y., and Harrison H. Zhou. "Minimax rates of community detection in stochastic block models." Annals of Statistics 44, no. 5 (October 2016): 2252–80. http://dx.doi.org/10.1214/15-aos1428.

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24

Schaub, Michael T., Santiago Segarra, and John N. Tsitsiklis. "Blind Identification of Stochastic Block Models from Dynamical Observations." SIAM Journal on Mathematics of Data Science 2, no. 2 (January 2020): 335–67. http://dx.doi.org/10.1137/19m1263340.

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25

Qiao, Maoying, Jun Yu, Wei Bian, Qiang Li, and Dacheng Tao. "Adapting Stochastic Block Models to Power-Law Degree Distributions." IEEE Transactions on Cybernetics 49, no. 2 (February 2019): 626–37. http://dx.doi.org/10.1109/tcyb.2017.2783325.

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26

Tabouy, Timothée, Pierre Barbillon, and Julien Chiquet. "Variational Inference for Stochastic Block Models From Sampled Data." Journal of the American Statistical Association 115, no. 529 (April 11, 2019): 455–66. http://dx.doi.org/10.1080/01621459.2018.1562934.

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27

Barucca, P., F. Lillo, P. Mazzarisi, and D. Tantari. "Disentangling group and link persistence in dynamic stochastic block models." Journal of Statistical Mechanics: Theory and Experiment 2018, no. 12 (December 21, 2018): 123407. http://dx.doi.org/10.1088/1742-5468/aaeb44.

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28

Eldan, Ronen, and Renan Gross. "Exponential random graphs behave like mixtures of stochastic block models." Annals of Applied Probability 28, no. 6 (December 2018): 3698–735. http://dx.doi.org/10.1214/18-aap1402.

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29

Funke, Thorben, and Till Becker. "Forecasting Changes in Material Flow Networks with Stochastic Block Models." Procedia CIRP 81 (2019): 1183–88. http://dx.doi.org/10.1016/j.procir.2019.03.289.

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30

Funke, Thorben, and Till Becker. "Stochastic block models: A comparison of variants and inference methods." PLOS ONE 14, no. 4 (April 23, 2019): e0215296. http://dx.doi.org/10.1371/journal.pone.0215296.

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31

Latouche, P., E. Birmelé, and C. Ambroise. "Variational Bayesian inference and complexity control for stochastic block models." Statistical Modelling: An International Journal 12, no. 1 (February 2012): 93–115. http://dx.doi.org/10.1177/1471082x1001200105.

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32

Pamfil, A. Roxana, Sam D. Howison, Renaud Lambiotte, and Mason A. Porter. "Relating Modularity Maximization and Stochastic Block Models in Multilayer Networks." SIAM Journal on Mathematics of Data Science 1, no. 4 (January 2019): 667–98. http://dx.doi.org/10.1137/18m1231304.

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33

GNOATTO, ALESSANDRO. "COHERENT FOREIGN EXCHANGE MARKET MODELS." International Journal of Theoretical and Applied Finance 20, no. 01 (February 2017): 1750007. http://dx.doi.org/10.1142/s0219024917500078.

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A model describing the dynamics of a foreign exchange (FX) rate should preserve the same level of analytical tractability when the inverted FX process is considered. We show that affine stochastic volatility models satisfy such a requirement. Such a finding allows us to use affine stochastic volatility models as a building block for FX dynamics that are functionally-invariant with respect to the construction of suitable products/ratios of rates, thus generalizing the model of [A. De Col, A. Gnoatto & M. Grasselli (2013) Smiles all around: FX joint calibration in a multi-Heston model, Journal of Banking and Finance 37 (10), 3799–3818.].
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34

Gölbaşı, Onur, and Nuray Demirel. "Stochastic Models in Preventive Maintenance Policies." Advanced Materials Research 1016 (August 2014): 802–6. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.802.

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In recent decades, philosophy behind maintenance has varied consistently due to the changes in complexity of designs, advances in automation and mechanization, adaptation to the fast growing market demand, commercial computation in the sectors, and environmental issues. In mid-forties, simplicity of designs, limited maintenance opportunities, and immaturity of trade culture made enough to performonly fix it when it brokeapproach, i.e. corrective maintenance, after failures. Last quarter of the 21thcentury made essential to constitute more conservative and preventive maintenance policies in order to ensure safety, reliability, and availability of systems with longer lifetime and cost effectiveness. Preventive maintenance can provide an economic saving more than 18% of operating cost of systems. In this basis, various stochastic models were proposed as a tool to constitute a maintenance policy to measure system availability and to obtain optimal maintenance periods. This paper presents a general perspective on common stochastic models in maintenance planning such as Homogenous Poisson Process, Non-Homogenous Poisson Process, and Imperfect Maintenance. The paper also introduces two common maintenance policies, block and age replacement policy, using these stochastic models.
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35

Fasino, Dario, and Francesco Tudisco. "The expected adjacency and modularity matrices in the degree corrected stochastic block model." Special Matrices 6, no. 1 (March 7, 2018): 110–21. http://dx.doi.org/10.1515/spma-2018-0010.

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Abstract We provide explicit expressions for the eigenvalues and eigenvectors of matrices that can be written as the Hadamard product of a block partitioned matrix with constant blocks and a rank one matrix. Such matrices arise as the expected adjacency or modularity matrices in certain random graph models that are widely used as benchmarks for community detection algorithms.
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36

Hwang, Jong Yun, Ji Oon Lee, and Wooseok Yang. "Local law and Tracy–Widom limit for sparse stochastic block models." Bernoulli 26, no. 3 (August 2020): 2400–2435. http://dx.doi.org/10.3150/20-bej1201.

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37

Zhang, Pan, Florent Krzakala, Jörg Reichardt, and Lenka Zdeborová. "Comparative study for inference of hidden classes in stochastic block models." Journal of Statistical Mechanics: Theory and Experiment 2012, no. 12 (December 20, 2012): P12021. http://dx.doi.org/10.1088/1742-5468/2012/12/p12021.

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38

Asai, Manabu, Massimiliano Caporin, and Michael McAleer. "Forecasting Value-at-Risk using block structure multivariate stochastic volatility models." International Review of Economics & Finance 40 (November 2015): 40–50. http://dx.doi.org/10.1016/j.iref.2015.02.004.

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39

Godoy-Lorite, Antonia, Roger Guimerà, Cristopher Moore, and Marta Sales-Pardo. "Accurate and scalable social recommendation using mixed-membership stochastic block models." Proceedings of the National Academy of Sciences 113, no. 50 (November 23, 2016): 14207–12. http://dx.doi.org/10.1073/pnas.1606316113.

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With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.
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40

Latouche, Pierre, Etienne Birmelé, and Christophe Ambroise. "Overlapping stochastic block models with application to the French political blogosphere." Annals of Applied Statistics 5, no. 1 (March 2011): 309–36. http://dx.doi.org/10.1214/10-aoas382.

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41

Menzio, Guido, and Shouyong Shi. "Block recursive equilibria for stochastic models of search on the job." Journal of Economic Theory 145, no. 4 (July 2010): 1453–94. http://dx.doi.org/10.1016/j.jet.2009.10.016.

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42

Omori, Yasuhiro, and Toshiaki Watanabe. "Block sampler and posterior mode estimation for asymmetric stochastic volatility models." Computational Statistics & Data Analysis 52, no. 6 (February 2008): 2892–910. http://dx.doi.org/10.1016/j.csda.2007.09.001.

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43

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.
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44

Napoli, M. L., M. Barbero, D. Minuto, L. Morandi, and H. Ullah. "The relict landslide in bimsoils in downtown Genova, Italy: a new modeling approach." IOP Conference Series: Earth and Environmental Science 1124, no. 1 (January 1, 2023): 012124. http://dx.doi.org/10.1088/1755-1315/1124/1/012124.

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Abstract Stability problems occurring in geological units with a block-in-matrix fabric are often analyzed with deterministic approaches and/or assuming block-in-matrix rocks/soils (bimrocks or bimsoils) to be homogeneous equivalent geomaterials. However, recent studies have demonstrated that since these formations are characterized by a great (dimensional, spatial and lithological) variability, reliable results can only be obtained if a stochastic approach accounting for different block arrangements and dimensions is used. This paper extends and improves a previous study from Minuto and Morandi (2015) to evaluate the stability of a relict landslide in bimsoil located in downtown Genova (Italy), where a deterministic approach and the traditional limit equilibrium method were used. In this work, different slope models with elliptical blocks of variable eccentricity, size and positions are generated by means of a stochastic approach and are analyzed with the FEM code RS2. Moreover, since the slope can be considered to be a bimsoil, interfaces between the blocks and matrix are introduced in order to better simulate the lower strength at the block/matrix contacts. The numerical analyses of the slope reveal that shallow failure surfaces have a higher probability of occurrence as compared to the deep failure surfaces considered by Minuto and Morandi (2015). Furthermore, lower safety factors are obtained when a block-matrix interface strength smaller than that of the matrix (i.e., a bimsoil) is simulated.
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45

Maleki, Mohammad, Enrique Jélvez, Xavier Emery, and Nelson Morales. "Stochastic Open-Pit Mine Production Scheduling: A Case Study of an Iron Deposit." Minerals 10, no. 7 (June 29, 2020): 585. http://dx.doi.org/10.3390/min10070585.

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Production planning decisions in the mining industry are affected by geological, geometallurgical, economic and operational information. However, the traditional approach to address this problem often relies on simplified models that ignore the variability and uncertainty of these parameters. In this paper, two main sources of uncertainty are combined to obtain multiple simulated block models in an iron ore deposit that include the rock type and seven quantitative variables (grades of Fe, SiO2, S, P and K, magnetic ratio and specific gravity). To assess the effect of integrating these two sources of uncertainty in mine planning decision, stochastic and deterministic production scheduling models are applied based on the simulated block models. The results show the capacity of the stochastic mine planning model to identify and minimize risks, obtaining valuable information in ore content or quality at early stages of the project, and improving decision-making with respect to the deterministic production scheduling. Numerically speaking, the stochastic mine planning model improves 6% expected cumulative discounted cash flow and generates 16% more iron ore than deterministic model.
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46

Gulikers, Lennart, Marc Lelarge, and Laurent Massoulié. "A spectral method for community detection in moderately sparse degree-corrected stochastic block models." Advances in Applied Probability 49, no. 3 (September 2017): 686–721. http://dx.doi.org/10.1017/apr.2017.18.

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AbstractWe consider community detection in degree-corrected stochastic block models. We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block membership of all but a vanishing fraction of nodes, in the regime where the lowest degree is of order log(n) or higher. Recovery succeeds even for very heterogeneous degree distributions. The algorithm does not rely on parameters as input. In particular, it does not need to know the number of communities.
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47

Mariadassou, Mahendra, and Catherine Matias. "Convergence of the groups posterior distribution in latent or stochastic block models." Bernoulli 21, no. 1 (February 2015): 537–73. http://dx.doi.org/10.3150/13-bej579.

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48

Yuan, Mingao, Yang Feng, and Zuofeng Shang. "A likelihood-ratio type test for stochastic block models with bounded degrees." Journal of Statistical Planning and Inference 219 (July 2022): 98–119. http://dx.doi.org/10.1016/j.jspi.2021.12.005.

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49

Peng, Chengbin, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, and David E. Keyes. "A scalable community detection algorithm for large graphs using stochastic block models." Intelligent Data Analysis 21, no. 6 (November 15, 2017): 1463–85. http://dx.doi.org/10.3233/ida-163156.

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50

Peng, Chengbin, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, and David E. Keyes. "A scalable community detection algorithm for large graphs using stochastic block models." Intelligent Data Analysis 22, no. 1 (February 22, 2018): 239. http://dx.doi.org/10.3233/ida-179999.

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