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Journal articles on the topic 'Scale-free'

To see the other types of publications on this topic, follow the link: Scale-free.

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Zhang, Linjun, Michael Small, and Kevin Judd. "Exactly scale-free scale-free networks." Physica A: Statistical Mechanics and its Applications 433 (September 2015): 182–97. http://dx.doi.org/10.1016/j.physa.2015.03.074.

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2

Barabási, Albert-László, and Eric Bonabeau. "Scale-Free Networks." Scientific American 288, no. 5 (May 2003): 60–69. http://dx.doi.org/10.1038/scientificamerican0503-60.

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3

R., Cesar, and Albert-Laszlo Barabasi. "Scale-free networks." Scholarpedia 3, no. 1 (2008): 1716. http://dx.doi.org/10.4249/scholarpedia.1716.

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4

Deijfen, Maria, Remco van der Hofstad, and Gerard Hooghiemstra. "Scale-free percolation." Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 49, no. 3 (August 2013): 817–38. http://dx.doi.org/10.1214/12-aihp480.

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5

Hein, Oliver, Michael Schwind, and Wolfgang König. "Scale-free networks." WIRTSCHAFTSINFORMATIK 48, no. 4 (August 2006): 267–75. http://dx.doi.org/10.1007/s11576-006-0058-2.

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6

Liu, Dong, Viktoria Fodor, and Lars Kildehoj Rasmussen. "Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?" IEEE Transactions on Network Science and Engineering 6, no. 3 (July 1, 2019): 587–98. http://dx.doi.org/10.1109/tnse.2018.2841942.

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7

Lehnert, R., P. Novák, F. Macieira, M. Kuřec, J. a. Teixeira, and T. Branyik. "Optimisation of lab-scale continuous alcohol-free beer production." Czech Journal of Food Sciences 27, No. 4 (September 9, 2009): 267–75. http://dx.doi.org/10.17221/128/2009-cjfs.

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In order to study the formation and conversion of the most important flavour compounds, the real wort used in alcohol-free beer fermentation was mimicked by a complex model medium containing glucose, yeast extract, and selected aldehydes. The fermentation experiments were carried out in a continuously operating gas-lift reactor with brewing yeast immobilised on spent grains (brewing by-product). During the continuous experiment, parameters such as oxygen supply, residence time (Rt), and temperature (T) were varied to find the optimal conditions for the alcohol-free beer production. The formation of ethanol, higher alcohols (HA), esters (ES), as well as the reduction of aldehydes and consumption of glucose were observed. The results suggest that the process parameters represent a powerful tool in controlling the degree of fermentation and flavour formation brought about by immobilised biocatalyst. Subsequently, the optimised process parameters were used to produce real alcohol-free beer during continuous fermentation. The final product was compared with batch fermented alcohol-free beers using the methods of instrumental and sensorial analysis.

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8

WOERLEE, GEERT FEYE. "Water-Free Dyeing of Textiles from Lab to Industrial Scale." Sen'i Gakkaishi 69, no. 10 (2013): P_341—P_342. http://dx.doi.org/10.2115/fiber.69.p_341.

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9

Graña, Matías, and Juan Pablo Pinasco. "Discrete scale invariance in scale free graphs." Physica A: Statistical Mechanics and its Applications 380 (July 2007): 601–10. http://dx.doi.org/10.1016/j.physa.2007.02.047.

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10

Pasetto, Stefano, Cesare Chiosi, Mark Cropper, and Eva K. Grebel. "Scale-free convection theory." Proceedings of the International Astronomical Union 11, A29B (August 2015): 747. http://dx.doi.org/10.1017/s1743921316006700.

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AbstractConvection is one of the fundamental mechanisms to transport energy, e.g., in planetology, oceanography, as well as in astrophysics where stellar structure is customarily described by the mixing-length theory, which makes use of the mixing-length scale parameter to express the convective flux, velocity, and temperature gradients of the convective elements and stellar medium. The mixing-length scale is taken to be proportional to the local pressure scale height of the star, and the proportionality factor (the mixing-length parameter) must be determined by comparing the stellar models to some calibrator, usually the Sun. No strong arguments exist to claim that the mixing-length parameter is the same in all stars and all evolutionary phases. Because of this, all stellar models in the literature are hampered by this basic uncertainty. In a recent paper (Pasetto et al. 2014) we presented the first fully analytical scale-free theory of convection that does not require the mixing-length parameter. Our self-consistent analytical formulation of convection determines all the properties of convection as a function of the physical behaviour of the convective elements themselves and the surrounding medium (be it a star, an ocean, or a primordial planet). The new theory of convection is formulated starting from a conventional solution of the Navier-Stokes/Euler equations, i.e. the Bernoulli equation for a perfect fluid, but expressed in a non-inertial reference frame co-moving with the convective elements. In our formalism, the motion of convective cells inside convective-unstable layers is fully determined by a new system of equations for convection in a non-local and time dependent formalism. We obtained an analytical, non-local, time-dependent solution for the convective energy transport that does not depend on any free parameter. The predictions of the new theory in astrophysical environment are compared with those from the standard mixing-length paradigm in stars with exceptional results for atmosphere models of the Sun and all the stars in the Hertzsprung-Russell diagram.

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11

Jeong, Hawoong. "Complex scale-free networks." Physica A: Statistical Mechanics and its Applications 321, no. 1-2 (April 2003): 226–37. http://dx.doi.org/10.1016/s0378-4371(02)01774-0.

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12

Barabási, Albert-László, Erzsébet Ravasz, and Tamás Vicsek. "Deterministic scale-free networks." Physica A: Statistical Mechanics and its Applications 299, no. 3-4 (October 2001): 559–64. http://dx.doi.org/10.1016/s0378-4371(01)00369-7.

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13

Scholz, Jan, Mathäus Dejori, Martin Stetter, and Martin Greiner. "Noisy scale-free networks." Physica A: Statistical Mechanics and its Applications 350, no. 2-4 (May 2005): 622–42. http://dx.doi.org/10.1016/j.physa.2004.11.012.

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14

Orabona, Francesco, and Dávid Pál. "Scale-free online learning." Theoretical Computer Science 716 (March 2018): 50–69. http://dx.doi.org/10.1016/j.tcs.2017.11.021.

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15

Miyoshi, Naoto, Takeya Shigezumi, Ryuhei Uehara, and Osamu Watanabe. "Scale free interval graphs." Theoretical Computer Science 410, no. 45 (October 2009): 4588–600. http://dx.doi.org/10.1016/j.tcs.2009.08.012.

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16

Freeman, Walter. "Scale-free neocortical dynamics." Scholarpedia 2, no. 2 (2007): 1357. http://dx.doi.org/10.4249/scholarpedia.1357.

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17

Copelli, M., and P. R. A. Campos. "Excitable scale free networks." European Physical Journal B 56, no. 3 (April 2007): 273–78. http://dx.doi.org/10.1140/epjb/e2007-00114-7.

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18

Fox Keller, Evelyn. "Revisiting “scale-free” networks." BioEssays 27, no. 10 (2005): 1060–68. http://dx.doi.org/10.1002/bies.20294.

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19

Small, Michael, Kevin Judd, and Thomas Stemler. "A surrogate for networks—How scale-free is my scale-free network?" IEICE Proceeding Series 2 (March 17, 2014): 236–39. http://dx.doi.org/10.15248/proc.2.236.

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20

Hirose, Kei, Yukihiro Ogura, and Hidetoshi Shimodaira. "ESTIMATING SCALE-FREE NETWORKS VIA THE EXPONENTIATION OF MINIMAX CONCAVE PENALTY." Journal of the Japanese Society of Computational Statistics 28, no. 1 (2015): 139–54. http://dx.doi.org/10.5183/jjscs.1503001_215.

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21

Lee, Kang-won, Hee-kwan Uhm, and Hye-jin Choe. "Tunable Network Generation Model for Small-World and Scale-Free Network." Journal of Korean Institute of Communications and Information Sciences 42, no. 7 (July 31, 2017): 1392–401. http://dx.doi.org/10.7840/kics.2017.42.7.1392.

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22

Martin, Nicolas, Paolo Frasca, and Carlos Canudas-de-Wit. "Large-Scale Network Reduction Towards Scale-Free Structure." IEEE Transactions on Network Science and Engineering 6, no. 4 (October 1, 2019): 711–23. http://dx.doi.org/10.1109/tnse.2018.2871348.

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23

Stumpf, M. P. H., C. Wiuf, and R. M. May. "Subnets of scale-free networks are not scale-free: Sampling properties of networks." Proceedings of the National Academy of Sciences 102, no. 12 (March 14, 2005): 4221–24. http://dx.doi.org/10.1073/pnas.0501179102.

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24

Lee, Kang-won, Jae-hoon Lee, and Hye-zin Choe. "Generalized Network Generation Method for Small-World Network and Scale-Free Network." Journal of Korean Institute of Communications and Information Sciences 41, no. 7 (July 31, 2016): 754–64. http://dx.doi.org/10.7840/kics.2016.41.7.754.

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25

Rajula, Hema Sekhar Reddy, Matteo Mauri, and Vassilios Fanos. "Scale-free networks in metabolomics." Bioinformation 14, no. 03 (March 31, 2018): 140–44. http://dx.doi.org/10.6026/97320630014140.

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26

Campos, Paulo R. A., and Viviane M. de Oliveira. "Scale-free networks in evolution." Physica A: Statistical Mechanics and its Applications 325, no. 3-4 (July 2003): 570–76. http://dx.doi.org/10.1016/s0378-4371(03)00245-0.

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27

WU, Jun, Yue-Jin TAN, Hong-Zhong DENG, and Da-Zhi ZHU. "Heterogeneity of Scale-free Networks." Systems Engineering - Theory & Practice 27, no. 5 (May 2007): 101–5. http://dx.doi.org/10.1016/s1874-8651(08)60036-8.

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28

Corso, G., J. E. Freitas, and L. S. Lucena. "A multifractal scale-free lattice." Physica A: Statistical Mechanics and its Applications 342, no. 1-2 (October 2004): 214–20. http://dx.doi.org/10.1016/j.physa.2004.04.081.

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29

Tao, Zhou, and Wang Bing-Hong. "Catastrophes in Scale-Free Networks." Chinese Physics Letters 22, no. 5 (April 14, 2005): 1072–75. http://dx.doi.org/10.1088/0256-307x/22/5/012.

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30

QIN, QIONG, ZHIPING WANG, FANG ZHANG, and PENGYUAN XU. "EVOLVING SCALE-FREE NETWORK MODEL." International Journal of Modern Physics B 22, no. 13 (May 20, 2008): 2139–49. http://dx.doi.org/10.1142/s0217979208039307.

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The Barabási–Albert (BA) model is extended here to include the concept of modifying the preferential attachment and combining the global preferential attachment with local preferential attachment. Our preferential attachment makes the nodes with higher degree increase less rapidly than the BA model after a long time. The maximum degree is introduced. We compare the time-evolution of the degree of the BA model and our model to illustrate that our model can control the degree of some nodes increasing dramatically with increasing time. Using the continuum theory and the rate equation method, we obtain the analytical expressions of the time-evolution of the degree and the power-law degree distribution.

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31

Krishnamurthy, Deepak, Hongquan Li, François Benoit du Rey, Pierre Cambournac, Adam G. Larson, Ethan Li, and Manu Prakash. "Scale-free vertical tracking microscopy." Nature Methods 17, no. 10 (August 17, 2020): 1040–51. http://dx.doi.org/10.1038/s41592-020-0924-7.

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32

Xie, Yan-Bo, Tao Zhou, and Bing-Hong Wang. "Scale-free networks without growth." Physica A: Statistical Mechanics and its Applications 387, no. 7 (March 2008): 1683–88. http://dx.doi.org/10.1016/j.physa.2007.11.005.

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33

Shen, Yue, and Yu-Qing Lou. "Gravitationally coupled scale-free discs." Monthly Notices of the Royal Astronomical Society 353, no. 1 (September 2004): 249–69. http://dx.doi.org/10.1111/j.1365-2966.2004.08065.x.

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34

Helbig, Thorsten, Jochen Riederer, Florian Kamp, and Matthias Oppe. "Free-form on every scale." Steel Construction 9, no. 3 (August 2016): 249–54. http://dx.doi.org/10.1002/stco.201620031.

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35

Abe, S., and N. Suzuki. "Scale-free network of earthquakes." Europhysics Letters (EPL) 65, no. 4 (February 2004): 581–86. http://dx.doi.org/10.1209/epl/i2003-10108-1.

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36

Goh, K. I., E. Oh, H. Jeong, B. Kahng, and D. Kim. "Classification of scale-free networks." Proceedings of the National Academy of Sciences 99, no. 20 (September 18, 2002): 12583–88. http://dx.doi.org/10.1073/pnas.202301299.

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37

Dos Santos, A. M., M. L. De Almeida, G. A. Mendes, and L. R. Da Silva. "Generalized scale-free homophilic network." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550097. http://dx.doi.org/10.1142/s0129183115500977.

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We propose a simple network growth process where the preferential attachment contains two essential parameters: homophily, namely, the tendency of sites to link with similar ones, and the number of attaching neighbors. It jointly generalizes the Barabási–Albert model and the scale-free homophilic model with a control parameter which tunes the importance of the homophily on preferential attachment process. Our results support a detailed discussion about different kinds of correlation, in special a fitness correlation introduced in this paper, and comparisons between BA model, scale-free homophilic model, and our present model considering its topological properties: degree distribution, time dependence of the connectivity and clustering coefficient.

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38

Alam, Maksudul, Maleq Khan, Kalyan S. Perumalla, and Madhav Marathe. "Generating Massive Scale-free Networks." ACM Transactions on Parallel Computing 7, no. 2 (May 31, 2020): 1–35. http://dx.doi.org/10.1145/3391446.

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39

Petermann, T., and P. De Los Rios. "Exploration of scale-free networks." European Physical Journal B - Condensed Matter and Complex Systems 38, no. 2 (March 2004): 201–4. http://dx.doi.org/10.1140/epjb/e2004-00021-5.

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40

Elahi, Pascal J., Robert J. Thacker, Lawrence M. Widrow, and Evan Scannapieco. "Subhaloes in scale-free cosmologies." Monthly Notices of the Royal Astronomical Society 395, no. 4 (June 1, 2009): 1950–62. http://dx.doi.org/10.1111/j.1365-2966.2009.14707.x.

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41

Gała̧zka, M., and J. Szymański. "Security of scale-free networks." Journal of Mathematical Sciences 182, no. 2 (March 17, 2012): 200–209. http://dx.doi.org/10.1007/s10958-012-0740-4.

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42

Dondajewski, M., and J. Szymański. "Branches in scale-free trees." Journal of Mathematical Sciences 161, no. 6 (September 2009): 961–68. http://dx.doi.org/10.1007/s10958-009-9615-8.

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43

Clote, P. "Are RNA networks scale-free?" Journal of Mathematical Biology 80, no. 5 (January 16, 2020): 1291–321. http://dx.doi.org/10.1007/s00285-019-01463-z.

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44

ZHAO, YULI, FRANCIS C. M. LAU, ZHILIANG ZHU, and HAI YU. "SCALE-FREE LUBY TRANSFORM CODES." International Journal of Bifurcation and Chaos 22, no. 04 (April 2012): 1250094. http://dx.doi.org/10.1142/s0218127412500940.

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This paper reports the characteristics and performance of a new type of Luby Transform codes, namely scale-free Luby Transform (SF-LT) codes. In the SF-LT codes, the degree of the encoded symbol follows a modified power-law distribution. Moreover, the complexity and decoding performance of SF-LT codes are compared with LT codes based on robust soliton degree distribution and LT codes based on suboptimal degree distribution. The results show that SF-LT codes outperform other LT codes in terms of the probability of successful decoding over an ideal channel and a binary erasure channel. Moreover, the encoding/decoding complexity for the SF-LT codes is superior.

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45

MATSUNAGA, Tsutomu, Shuhei KUWATA, and Masaaki MURAMATSU. "Empirical analysis of scale-free patterns of connectivity in medical term occurrence." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 2 (2015): 616–20. http://dx.doi.org/10.3156/jsoft.27.616.

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46

Finke, J., N. Quijano, and K. M. Passino. "Emergence of scale-free networks from ideal free distributions." EPL (Europhysics Letters) 82, no. 2 (March 26, 2008): 28004. http://dx.doi.org/10.1209/0295-5075/82/28004.

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47

Choi, WooSeok, Ashtosh Sharma, Shizhi Qian, Geunbae Lim, and Sang Woo Joo. "Is free surface free in micro-scale electrokinetic flows?" Journal of Colloid and Interface Science 347, no. 1 (July 2010): 153–55. http://dx.doi.org/10.1016/j.jcis.2010.03.049.

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48

Yiqun Huang, Jiayin Chen, Lin Pei, and Yongliang Shi. "Enhance Robustness of Scale-free Networks." International Journal of Advancements in Computing Technology 3, no. 7 (August 31, 2011): 17–22. http://dx.doi.org/10.4156/ijact.vol3.issue7.3.

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49

Lou, Shun-Li, and Xu-Hua Yang. "Random Connection Based Scale-free Networks." International Journal of Information Technology and Computer Science 5, no. 6 (May 1, 2013): 10–15. http://dx.doi.org/10.5815/ijitcs.2013.06.02.

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50

Katona, Zsolt. "Width of a scale-free tree." Journal of Applied Probability 42, no. 03 (September 2005): 839–50. http://dx.doi.org/10.1017/s0021900200000814.

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Consider the random graph model of Barabási and Albert, where we add a new vertex in every step and connect it to some old vertices with probabilities proportional to their degrees. If we connect it to only one of the old vertices then this will be a tree. These graphs have been shown to have a power-law degree distribution, the same as that observed in some large real-world networks. We are interested in the width of the tree and we show that it is at the nth step; this also holds for a slight generalization of the model with another constant. We then see how this theoretical result can be applied to directory trees.

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