Albert, R. & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1): 47–49. https://doi.org/10.1103/RevModPhys.74.47
Bafna, P., Pramod, D. & Vaidya, A. (2018). Document clustering: TF-IDF approach. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). Neurocomputing, 300: 70-79.
Barabasi, A. & Bonabeau, E. (2003). Scale-Free Networks. Scientific American, 288(5): 50–59. https://doi.org/10.1038/scientificamerican0503-60. PMID: 12701331
Barabási, A.L. & Albert, R. (2017). Emergence of scaling in random networks. Science, 286(15): 509-512.
Baran-Gale, J. & et al. (2020). Ageing compromises mouse thymus function and remodels epithelial cell differentiation. eLife, 9: e56221
Bassett, D.S. & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3): 353–364. https://doi.org/10.1038/nn.4502
Bauer, A., Hoedoro, N. & Schneider, A. (2015). Rule-based Approach to Text Generation in Natural Language-Automated Text Markup Language (ATML3). In: Challenge+DC@RuleML.
Benson, A.R., Gleich, D.F. & Leskovec, J. (2016). Higher-order organization of complex networks. Science, 353(6295): 163-166.
Breuer, A., Elflein, S., Joseph, T., Termöhlen, J., Homoceanu, S. & Fingscheidt, T. (2019). Analysis of the effect of various input representations for LSTM-based trajectory prediction. IEEE Intell Transp Syst Conf (ITSC): 2728–2735.
Cancho, R.F.I. & Solé, R.V. (2001). The small world of human language. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1482): 2261-2265.
Chen, G. & Lou, Y. (2019). Multi-Language Naming Game. In: Naming Game. Springer: 135-154.
Chen, H., Chen, X. & Liu, H. (2018). How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks. PloS one, 13(2): e0192545.
Chitradurga, R. & Helmy, A. (2014). Analysis of wired short cuts in wireless sensor networks. IEEE/ACS International Conference on, Pervasive Services: 167-176.
Cinar, I., Koklu, M. & Tasdemir, S. (2020). Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods. https://doi.org/10.30855/gmbd.2020.03.03.
da Fontoura Costa, L. (2021). A caleidoscope of datasets represented as networks by the coincidence methodology. URL=
https://researchgate.net/publication/356392287_A_Caleidoscope_of_Datasets_Represented_as_Networks_by_the_ Coincidence Methodology.
Fornito, A. (2020). An Introduction to Network Neuroscience: How to build, model, and analyse connectomes - 0800-10:00 | OHBM". URL=
https://www.pathlms.com/ohbm/courses/12238/sections/15846/video_presentations/13753
Sajjadi, M.B. & Minaei Bidgoli, B. (2018). Persian language knowledge graph system architecture. Journal of Information Processing and Management, 35(2). [in persian]
Daud, A., Khan, W. & Che, D. (2017). Urdu language processing: a survey. Artificial Intelligence Review, 47(3): 279-311.
Fortunato, S. (2018). Community structure in complex networks. in EGC.
Fromkin, V., Rodman, R. & Hyam, N. )2018(. An introduction to language Cengage Learning. Michael Rosenberg.
Gao, Z.-K., Small, M. & Kurths, J. (2017). Complex network analysis of time series. EPL, 116(5): 50001.
Garnham, A. (2017). Artificial intelligence an introduction. Routledge.
Goh, W.P., Luke, K.-K. & Cheong, S.A. (2018). Functional shortcuts in language co-occurrence networks. PloS one, 13(9): e0203025.
Helmy, A. (2018). Small worlds in wireless networks. IEEE Commun Lett, 7(10): 490-492.
Howard., J. & Ruder, S. (2018). Universal language model fine-tuning for text classification. In: Annual Meeting of the Association for Computational Linguistics: 328–339.
Joulin, A. & et al. (2016). Bag of tricks for efficient text classification. arXiv preprint arXiv, 1607.01759.
Khan, N., Bakht, M.P. &d Waga, R.A. (2019). Corpus Construction and Structure Study of Urdu Language using Empirical Laws. Urdu News Headline, Text Classification by Using Different Machine Learning Algorithms.
Kiselev, V.Y., Andrews, T.S. & Hemberg, M. (2019). Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet., 20: 273–282.
LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature, 521(7553): 436.
https://doi.org/10.1038/nature14539
Lin, C., King, J., Bharadwaj, P., Chen, C., Gupta, A., Ding, W. & Prasad, M. (2019). EOG-based eye movement classification and application on HCI baseball game. IEEE Access, 7: 96166–96176.
Lucas, J., Tucker, G., Grosse, R. & Norouzi, M. (2019). Understanding posterior collapse in generative latent variable models. URL= https://openreview.net/pdf?id=r1xaVLUYuE
Newma, M. (2010). Networks: An Introduction. Oxford University Press.
Newman, M.E.J. & Watts, D.J. (2010). Renormalization group analysis of the small-world network model. Physics Letter A, vol. 263: 341-346.
Paul, G., Cao, F., Huang, Q.T., Wang, H.S., Gu, Q., Zhang, K., Shao, M. & Li., Y. (2018). An EOG-based human-machine interface for wheelchair control. IEEE Trans Biomed Eng, 65: 2023–2032.
Piryonesi, S.M., & Tamer, E.D. (2020). Role of Data Analytics in Infrastructure Asset Management: Overcoming Data Size and Quality Problems. Journal of Transportation Engineering, Part B Pavements,146(2).
Robert, C. (2014). Machine learning, a probabilistic perspective. Taylor & Francis.
Russell, S.J. & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia: Pearson Education Limited.
Saberi, M., Khosrowabadi, R., Khatibi, A., Misic, B. & Jafari, G. (2021). Topological impact of negative links on the stability of resting-state brain network. Scientific Reports, 11(1): 2176. https://doi.org/10.1038/s41598-021-81767-7
Siegel, J.S. & et al. (2018). Re-emergence of modular brain networks in stroke recovery. Cortex, 101: 44-59.
Stanley, H.E., Amaral, L.A.N., Scala, A. & Barthelemy, M. (2000). Classes of small-world networks. PNAS, 97(21): 11149–52. https://doi.org/10.1073/pnas.200327197.
Strogatz, S. & Watts, D.J. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684): 440–442. https://doi.org/10.1038/30918.
Sun, C. Qiu, X., Xu, Y. & Huang, X. (2019). How to fine-tune BERT for text classification? in: China National Conference on Chinese Computational Linguistics: 194–206.
Vijaymeena., M.K. & Kavitha, K. (2016). A survey on similarity measures in text mining. Machine Learning and Applications: An International Journal, 3(1): 19–28.
Wilhelm, T. & Kim, J. (2008). What is a complex graph? Physica A: Statistical Mechanics and its Applications, 387(11): 2637–2652. https://doi.org/10.1016/j.physa.2008.01.015.
Xie, Q., Dai, Z., Hovy, E., Luong, M.T. & Le, Q.V. (2020). Unsupervised data augmentation for consistency training. in: Annual Conference on Neural Information Processing Systems.
Yang, H., Cheng, J., Yang, Z., Zhang, H., Zhang, W., Yang, K. & Chen, X. (2021). A node similarity and community link strength-based community discovery algorithm Complexity. Complexity, 22:1-17. https://doi.org/10.1155/2021/8848566
Yule, C.U. (2014). The statistical study of literary vocabulary. Cambridge University Press.
Zhang, B., Zhou, W., CaiH, S., Wang, J., Zhang, Z. & Lei, T. (2020). Ubiquitous depression detection of sleep physiological data by using combination learning and functional networks. IEEE. https://doi.org/10.1109/ACCESS.2020.2994985
Zhang, Y., Gan, Z., Fan, K., Chen, Z., Henao, R., Shen, D. & et al. (2017). Adversarial feature matching for text generation. URL= https://arxiv.org/pdf/1706.03850.pdf
Send comment about this article