- -

Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data

Show full item record

Perez-Benito, FJ.; Garcia-Gomez, JM.; Navarro Pardo, E.; Conejero, JA. (2020). Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data. Mathematical Methods in the Applied Sciences. 43(14):8290-8301. https://doi.org/10.1002/mma.6567

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/166901

Files in this item

Item Metadata

Title: Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data
Author: Perez-Benito, Francisco Javier Garcia-Gomez, Juan M. NAVARRO PARDO, ESPERANZA Conejero, J. Alberto
UPV Unit: Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Universitat Politècnica de València. Instituto Universitario de Matemática Pura y Aplicada - Institut Universitari de Matemàtica Pura i Aplicada
Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Issued date:
Abstract:
[EN] Deep neural networks (DNNs) have emerged as a state-of-the-art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. ...[+]
Subjects: Automatic architecture , Community detection , Community-detection deep neural network (CD-DNN) , Deep learning , Happiness , Network science , Psychometric scales , Regression
Copyrigths: Reserva de todos los derechos
Source:
Mathematical Methods in the Applied Sciences. (issn: 0170-4214 )
DOI: 10.1002/mma.6567
Publisher:
John Wiley & Sons
Publisher version: https://doi.org/10.1002/mma.6567
Project ID:
info:eu-repo/grantAgreement/EC/H2020/727560/EU/Collective wisdom driving public health policies/
info:eu-repo/grantAgreement/EC/H2020/825750/EU/Patient-centred pathways of early palliative care, supportive ecosystems and appraisal standard/
Thanks:
The authors thank the support of the project Analysis, quality, and variability of medical data funded by Universitat Politècnica de València. JMGG and JAC acknowledge the support of the H2020 project CrowdHealth (Collective ...[+]
Type: Artículo

References

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539

Antonov, V., Tarkhov, D., & Vasilyev, A. (2018). Unified approach to constructing the neural network models of real objects. Part 1. Mathematical Methods in the Applied Sciences, 41(18), 9244-9251. doi:10.1002/mma.5205

Arifovic, J., & Gençay, R. (2001). Using genetic algorithms to select architecture of a feedforward artificial neural network. Physica A: Statistical Mechanics and its Applications, 289(3-4), 574-594. doi:10.1016/s0378-4371(00)00479-9 [+]
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539

Antonov, V., Tarkhov, D., & Vasilyev, A. (2018). Unified approach to constructing the neural network models of real objects. Part 1. Mathematical Methods in the Applied Sciences, 41(18), 9244-9251. doi:10.1002/mma.5205

Arifovic, J., & Gençay, R. (2001). Using genetic algorithms to select architecture of a feedforward artificial neural network. Physica A: Statistical Mechanics and its Applications, 289(3-4), 574-594. doi:10.1016/s0378-4371(00)00479-9

IslamB‐U BaharudinZ RazaM‐Q NallagowndenP.Optimization of neural network architecture using genetic algorithm for load forecasting. In: 5th International Conference on Intelligent and Advanced Systems (ICIAS) 2014;2014:1‐6.

KoutníkJ SchmidhuberJ GomezF.Evolving deep unsupervised convolutional networks for vision‐based reinforcement learning. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation ACM;2014:541‐548.

VidnerováP NerudaR.Evolving keras architectures for sensor data analysis Federated Conference on Computer Science and Information Systems (FedCSIS) 2017IEEE;2017:109‐112.

Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47-97. doi:10.1103/revmodphys.74.47

Newman, M., Barabási, A.-L., & Watts, D. J. (2011). The Structure and Dynamics of Networks. doi:10.1515/9781400841356

Albert, R., Jeong, H., & Barabási, A.-L. (1999). Diameter of the World-Wide Web. Nature, 401(6749), 130-131. doi:10.1038/43601

Redner, S. (1998). How popular is your paper? An empirical study of the citation distribution. The European Physical Journal B, 4(2), 131-134. doi:10.1007/s100510050359

Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M., & Sakaki, Y. (2001). A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences, 98(8), 4569-4574. doi:10.1073/pnas.061034498

BarabásiA‐L.Network medicine ‐ from obesity to the diseasom:Mass Medical Soc;2007.

Jian, F., & Dandan, S. (2016). Complex Network Theory and Its Application Research on P2P Networks. Applied Mathematics and Nonlinear Sciences, 1(1), 45-52. doi:10.21042/amns.2016.1.00004

FortunatoS CastellanoC.Community structure in graphs. In: Computational complexity;2012:490‐512.

Kernighan, B. W., & Lin, S. (1970). An Efficient Heuristic Procedure for Partitioning Graphs. Bell System Technical Journal, 49(2), 291-307. doi:10.1002/j.1538-7305.1970.tb01770.x

Scott, J. (2017). Social Network Analysis. doi:10.4135/9781529716597

Amaral, L. A. N., Scala, A., Barthelemy, M., & Stanley, H. E. (2000). Classes of small-world networks. Proceedings of the National Academy of Sciences, 97(21), 11149-11152. doi:10.1073/pnas.200327197

Marchiori, M., & Latora, V. (2000). Harmony in the small-world. Physica A: Statistical Mechanics and its Applications, 285(3-4), 539-546. doi:10.1016/s0378-4371(00)00311-3

Luo, W., Lu, N., Ni, L., Zhu, W., & Ding, W. (2020). Local community detection by the nearest nodes with greater centrality. Information Sciences, 517, 377-392. doi:10.1016/j.ins.2020.01.001

YanardagP VishwanathanS‐V‐N.Deep graph kernels. In: Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining;2015:1365‐1374.

LiJ ZhangH HanZ RongY ChengH HuangJ.Adversarial attack on community detection by hiding individuals. In: Proceedings of the web conference 2020;2020:917‐927.

Khodayar, M., & Wang, J. (2019). Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting. IEEE Transactions on Sustainable Energy, 10(2), 670-681. doi:10.1109/tste.2018.2844102

Pérez-Benito, F. J., Villacampa-Fernández, P., Conejero, J. A., García-Gómez, J. M., & Navarro-Pardo, E. (2019). A happiness degree predictor using the conceptual data structure for deep learning architectures. Computer Methods and Programs in Biomedicine, 168, 59-68. doi:10.1016/j.cmpb.2017.11.004

Spector, P. (1992). Summated Rating Scale Construction. doi:10.4135/9781412986038

Cacioppo, J. T., & Berntson, G. G. (1994). Relationship between attitudes and evaluative space: A critical review, with emphasis on the separability of positive and negative substrates. Psychological Bulletin, 115(3), 401-423. doi:10.1037/0033-2909.115.3.401

Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2). doi:10.1103/physreve.69.026113

Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6). doi:10.1103/physreve.70.066111

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008

Arenas, A., Duch, J., Fernández, A., & Gómez, S. (2007). Size reduction of complex networks preserving modularity. New Journal of Physics, 9(6), 176-176. doi:10.1088/1367-2630/9/6/176

Traag, V. A. (2015). Faster unfolding of communities: Speeding up the Louvain algorithm. Physical Review E, 92(3). doi:10.1103/physreve.92.032801

HagbergAric SwartPieter S ChultDaniel.Exploring network structure dynamics and function using networkx  Los Alamos National Lab.(LANL) Los Alamos NM (United States);2008.

AbadiM BarhamP ChenJ et al.Tensorflow: a system for large‐scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16);2016:265‐283.

Joseph, S., Linley, P. A., Harwood, J., Lewis, C. A., & McCollam, P. (2004). Rapid assessment of well-being: The Short Depression-Happiness Scale (SDHS). Psychology and Psychotherapy: Theory, Research and Practice, 77(4), 463-478. doi:10.1348/1476083042555406

Carver, C. S. (1997). You want to measure coping but your protocol’ too long: Consider the brief cope. International Journal of Behavioral Medicine, 4(1), 92-100. doi:10.1207/s15327558ijbm0401_6

Francis, L. J., Brown, L. B., & Philipchalk, R. (1992). The development of an abbreviated form of the revised Eysenck personality questionnaire (EPQR-A): Its use among students in England, Canada, the U.S.A. and Australia. Personality and Individual Differences, 13(4), 443-449. doi:10.1016/0191-8869(92)90073-x

Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science & Medicine, 32(6), 705-714. doi:10.1016/0277-9536(91)90150-b

LawrenceS GilesC‐L TsoiA‐C.Lessons in neural network training: overfitting may be harder than expected. In: AAAI/IAAI;1997:540‐545.

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record