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Journal articles on the topic 'Preferential attachment'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Tameirão, Cinthya Rocha, Sérgio Fernando Loureiro Rezende, and Luciana Pereira de Assis. "Ligação Preferencial e Aptidão na Evolução da Rede de Filmes Brasileiros." Organizações & Sociedade 28, no. 99 (2021): 888–916. http://dx.doi.org/10.1590/1984-92302021v28n9907pt.

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Abstract This study analyzes the network evolution, specifically that of the Brazilian film network. It examines two generative mechanisms that lie behind the network evolution: preferential attachment and fitness. The starting point is that preferential attachment and fitness may compete to shape the network evolution. We built a novel dataset with 974 Brazilian feature films released between 1995 and 2017 and used PAFit, a brand-new statistical method, to estimate the joint effects of preferential attachment and fitness on the evolution of the Brazilian film network. This study concludes tha

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2

Janson, Svante, Subhabrata Sen, and Joel Spencer. "Preferential attachment when stable." Advances in Applied Probability 51, no. 4 (2019): 1067–108. http://dx.doi.org/10.1017/apr.2019.42.

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AbstractWe study an urn process with two urns, initialized with a ball each. Balls are added sequentially, the urn being chosen independently with probability proportional to the $\alpha$th power $(\alpha >1)$ of the existing number of balls. We study the (rare) event that the urn compositions are balanced after the addition of $2n-2$ new balls. We derive precise asymptotics of the probability of this event by embedding the process in continuous time. Quite surprisingly, fine control of this probability may be leveraged to derive a lower-tail large deviation principle (LDP) for $L = \sum_{i

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3

Haslegrave, John, and Jonathan Jordan. "Preferential attachment with choice." Random Structures & Algorithms 48, no. 4 (2015): 751–66. http://dx.doi.org/10.1002/rsa.20616.

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4

Tameirão, Cinthya Rocha, Sérgio Fernando Loureiro Rezende, and Luciana Pereira de Assis. "Preferential Attachment and Fitness in the Evolution of the Brazilian Film Network." Organizações & Sociedade 28, no. 99 (2021): 888–916. http://dx.doi.org/10.1590/1984-92302021v28n9907en.

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Abstract This study analyzes the network evolution, specifically that of the Brazilian film network. It examines two generative mechanisms that lie behind the network evolution: preferential attachment and fitness. The starting point is that preferential attachment and fitness may compete to shape the network evolution. We built a novel dataset with 974 Brazilian feature films released between 1995 and 2017 and used PAFit, a brand-new statistical method, to estimate the joint effects of preferential attachment and fitness on the evolution of the Brazilian film network. This study concludes tha

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5

Chen, Chen. "The origin of preferential attachment and the generalized preferential attachment for weighted networks." Physica A: Statistical Mechanics and its Applications 377, no. 2 (2007): 709–16. http://dx.doi.org/10.1016/j.physa.2006.11.082.

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6

de Blasio, B. F., A. Svensson, and F. Liljeros. "Preferential attachment in sexual networks." Proceedings of the National Academy of Sciences 104, no. 26 (2007): 10762–67. http://dx.doi.org/10.1073/pnas.0611337104.

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7

Garavaglia, Alessandro, and Clara Stegehuis. "Subgraphs in preferential attachment models." Advances in Applied Probability 51, no. 03 (2019): 898–926. http://dx.doi.org/10.1017/apr.2019.36.

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AbstractWe consider subgraph counts in general preferential attachment models with power-law degree exponent $\tau > 2$ . For all subgraphs H, we find the scaling of the expected number of subgraphs as a power of the number of vertices. We prove our results on the expected number of subgraphs by defining an optimization problem that finds the optimal subgraph structure in terms of the indices of the vertices that together span it and by using the representation of the preferential attachment model as a Pólya urn model.

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8

Wu, Yan, Tom Z. J. Fu, and Dah Ming Chiu. "Generalized preferential attachment considering aging." Journal of Informetrics 8, no. 3 (2014): 650–58. http://dx.doi.org/10.1016/j.joi.2014.06.002.

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9

Dommers, Sander, Remco van der Hofstad, and Gerard Hooghiemstra. "Diameters in Preferential Attachment Models." Journal of Statistical Physics 139, no. 1 (2010): 72–107. http://dx.doi.org/10.1007/s10955-010-9921-z.

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10

ANTUNOVIĆ, TONĆI, ELCHANAN MOSSEL, and MIKLÓS Z. RÁCZ. "Coexistence in Preferential Attachment Networks." Combinatorics, Probability and Computing 25, no. 6 (2016): 797–822. http://dx.doi.org/10.1017/s0963548315000383.

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We introduce a new model of competition on growing networks. This extends the preferential attachment model, with the key property that node choices evolve simultaneously with the network. When a new node joins the network, it chooses neighbours by preferential attachment, and selects its type based on the number of initial neighbours of each type. The model is analysed in detail, and in particular, we determine the possible proportions of the various types in the limit of large networks. An important qualitative feature we find is that, in contrast to many current theoretical models, often se

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11

Lehmann, S., A. D. Jackson, and B. Lautrup. "Life, death and preferential attachment." Europhysics Letters (EPL) 69, no. 2 (2005): 298–303. http://dx.doi.org/10.1209/epl/i2004-10331-2.

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12

Lim, Chjan, and Weituo Zhang. "Relevance and Importance Preferential Attachment." Complex Systems 28, no. 3 (2019): 333–56. http://dx.doi.org/10.25088/complexsystems.28.3.333.

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13

Zuev, Konstantin, Fragkiskos Papadopoulos, and Dmitri Krioukov. "Hamiltonian dynamics of preferential attachment." Journal of Physics A: Mathematical and Theoretical 49, no. 10 (2016): 105001. http://dx.doi.org/10.1088/1751-8113/49/10/105001.

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14

Hruz, Tomas, and Ueli Peter. "Nongrowing Preferential Attachment Random Graphs." Internet Mathematics 6, no. 4 (2011): 461–87. http://dx.doi.org/10.1080/15427951.2010.553143.

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15

Siew, Cynthia S. Q., and Michael S. Vitevitch. "Investigating the Influence of Inverse Preferential Attachment on Network Development." Entropy 22, no. 9 (2020): 1029. http://dx.doi.org/10.3390/e22091029.

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Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the networ

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16

QIN, QIONG, ZHIPING WANG, FANG ZHANG, and PENGYUAN XU. "EVOLVING SCALE-FREE NETWORK MODEL." International Journal of Modern Physics B 22, no. 13 (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 o

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17

SANTIAGO, A., and R. M. BENITO. "EMERGENCE OF MULTISCALING IN HETEROGENEOUS COMPLEX NETWORKS." International Journal of Modern Physics C 18, no. 10 (2007): 1591–607. http://dx.doi.org/10.1142/s0129183107011571.

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In this paper we provide numerical evidence of the richer behavior of the connectivity degrees in heterogeneous preferential attachment networks in comparison to their homogeneous counterparts. We analyze the degree distribution in the threshold model, a preferential attachment model where the affinity between node states biases the attachment probabilities of links. We show that the degree densities exhibit a power-law multiscaling which points to a signature of heterogeneity in preferential attachment networks. This translates into a power-law scaling in the degree distribution, whose expone

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18

Lachgar, A., and A. Achahbar. "Network growth with preferential attachment and without “rich get richer” mechanism." International Journal of Modern Physics C 27, no. 02 (2015): 1650020. http://dx.doi.org/10.1142/s0129183116500200.

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We propose a simple preferential attachment model of growing network using the complementary probability of Barabási–Albert (BA) model, i.e. [Formula: see text]. In this network, new nodes are preferentially attached to not well connected nodes. Numerical simulations, in perfect agreement with the master equation solution, give an exponential degree distribution. This suggests that the power law degree distribution is a consequence of preferential attachment probability together with “rich get richer” phenomena. We also calculate the average degree of a target node at time t[Formula: see text]

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19

Малышкин, Юрий Андреевич. "PREFERENTIAL ATTACHMENT WITH FITNESS DEPENDENT CHOICE." Physical and Chemical Aspects of the Study of Clusters, Nanostructures and Nanomaterials, no. 13 (December 23, 2021): 483–94. http://dx.doi.org/10.26456/pcascnn/2021.13.483.

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Исследуется асимптотическое поведение максимальной степени вершины в графе предпочтительного присоединения с выбором вершины, основанном как на ее степени, так и на дополнительном параметре (пригодности). Модели предпочтительного присоединения широко используются для моделирования сложных сетей (таких как нейронные сети и т.д.). Они строятся следующим образом. Мы начинаем с двух вершин и ребра между ними. Затем на каждом шаге мы рассматриваем выборку из уже существующих вершин, выбранных с вероятностями, пропорциональными их степеням плюс некоторый параметр β>- 1. Затем мы добавляем новую в

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20

Wan, Phyllis, Tiandong Wang, Richard A. Davis, and Sidney I. Resnick. "Fitting the linear preferential attachment model." Electronic Journal of Statistics 11, no. 2 (2017): 3738–80. http://dx.doi.org/10.1214/17-ejs1327.

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21

Ben-Naim, E., and P. L. Krapivsky. "Stratification in the preferential attachment network." Journal of Physics A: Mathematical and Theoretical 42, no. 47 (2009): 475001. http://dx.doi.org/10.1088/1751-8113/42/47/475001.

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22

Borrel, V., M. Dias de Amorim, and S. Fdida. "A preferential attachment gathering mobility model." IEEE Communications Letters 9, no. 10 (2005): 900–902. http://dx.doi.org/10.1109/lcomm.2005.10023.

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23

Kasthurirathna, Dharshana, and Mahendra Piraveenan. "Cyclic Preferential Attachment in Complex Networks." Procedia Computer Science 18 (2013): 2086–94. http://dx.doi.org/10.1016/j.procs.2013.05.378.

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24

Rezaei, Soghra, Hanieh Moghaddasi, and Amir Hossein Darooneh. "Preferential attachment in evolutionary earthquake networks." Physica A: Statistical Mechanics and its Applications 495 (April 2018): 172–79. http://dx.doi.org/10.1016/j.physa.2017.12.063.

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25

Weaver, Iain S. "Preferential attachment in randomly grown networks." Physica A: Statistical Mechanics and its Applications 439 (December 2015): 85–92. http://dx.doi.org/10.1016/j.physa.2015.06.019.

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26

Jeong, H., Z. Néda, and A. L. Barabási. "Measuring preferential attachment in evolving networks." Europhysics Letters (EPL) 61, no. 4 (2003): 567–72. http://dx.doi.org/10.1209/epl/i2003-00166-9.

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27

Raigorodskii, A. M. "Small subgraphs in preferential attachment networks." Optimization Letters 11, no. 2 (2015): 249–57. http://dx.doi.org/10.1007/s11590-015-0945-9.

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28

Hansen, Jennie C., and Jerzy Jaworski. "A random mapping with preferential attachment." Random Structures and Algorithms 34, no. 1 (2009): 87–111. http://dx.doi.org/10.1002/rsa.20251.

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29

Giroire, Frédéric, Nicolas Nisse, Thibaud Trolliet, and Małgorzata Sulkowska. "Preferential attachment hypergraph with high modularity." Network Science 10, no. 4 (2022): 400–429. http://dx.doi.org/10.1017/nws.2022.35.

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AbstractNumerous works have been proposed to generate random graphs preserving the same properties as real-life large-scale networks. However, many real networks are better represented by hypergraphs. Few models for generating random hypergraphs exist, and also, just a few models allow to both preserve a power-law degree distribution and a high modularity indicating the presence of communities. We present a dynamic preferential attachment hypergraph model which features partition into communities. We prove that its degree distribution follows a power-law, and we give theoretical lower bounds f

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30

SHAO, CHEN-XI, HUI-LING DOU, RONG-XU YANG, and BING-HONG WANG. "ZERO NODES EFFECT: VALID LINK PREDICTION IN SPARSE NETWORKS." International Journal of Modern Physics B 27, no. 12 (2013): 1350052. http://dx.doi.org/10.1142/s0217979213500525.

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Zero-degree nodes are an important difficulty in sparse networks' link prediction. Clustering and preferential attachment, as the most important characteristics of complex networks, have been paid little attention in similarity indices. Inspired by the coexistence of clustering and preferential attachment in real networks, this paper proposes a new preferential attachment index and new clustering index, which have here been integrated into a hybrid index that considers the dynamic evolutionary forces of complex networks and can solve the problem of excessive zero-degree nodes in sparse network

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31

Takei, Masato. "Preferential Attachment Models and Reinforced Random Walks." Brain & Neural Networks 21, no. 4 (2014): 170–81. http://dx.doi.org/10.3902/jnns.21.170.

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32

Baur, Michael, Marco Gaertler, Robert Görke, Marcus Krug, and Dorothea Wagner. "Augmenting $k$-core generation with preferential attachment." Networks & Heterogeneous Media 3, no. 2 (2008): 277–94. http://dx.doi.org/10.3934/nhm.2008.3.277.

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33

Dereich, Steffen, and Peter Mörters. "Random Networks with Concave Preferential Attachment Rule." Jahresbericht der Deutschen Mathematiker-Vereinigung 113, no. 1 (2010): 21–40. http://dx.doi.org/10.1365/s13291-010-0011-6.

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34

Jian-Jun, Wu, Gao Zi-You, Sun Hui-Jun, and Huang Hai-Jun. "Random and Preferential Attachment Networks with Aging." Chinese Physics Letters 22, no. 3 (2005): 765–68. http://dx.doi.org/10.1088/0256-307x/22/3/068.

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35

D'Souza, R. M., C. Borgs, J. T. Chayes, N. Berger, and R. D. Kleinberg. "Emergence of tempered preferential attachment from optimization." Proceedings of the National Academy of Sciences 104, no. 15 (2007): 6112–17. http://dx.doi.org/10.1073/pnas.0606779104.

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36

Prokhorenkova, L. A., and A. V. Krot. "Local clustering coefficients in preferential attachment models." Doklady Mathematics 94, no. 3 (2016): 623–26. http://dx.doi.org/10.1134/s1064562416060041.

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37

Hajek, Bruce, and Suryanarayana Sankagiri. "Community Recovery in a Preferential Attachment Graph." IEEE Transactions on Information Theory 65, no. 11 (2019): 6853–74. http://dx.doi.org/10.1109/tit.2019.2927624.

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38

Jordan, Jonathan. "Degree sequences of geometric preferential attachment graphs." Advances in Applied Probability 42, no. 2 (2010): 319–30. http://dx.doi.org/10.1239/aap/1275055230.

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We investigate the degree sequence of the geometric preferential attachment model of Flaxman, Frieze and Vera (2006), (2007) in the case where the self-loop parameter α is set to 0. We show that, given certain conditions on the attractiveness function F, the degree sequence converges to the same sequence as found for standard preferential attachment in Bollobás et al. (2001). We also apply our method to the extended model introduced in van der Esker (2008) which allows for an initial attractiveness term, proving similar results.

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39

SANTIAGO, ANTONIO, and ROSA M. BENITO. "EVOLUTION OF HETEROGENEOUS NETWORKS UNDER PREFERENTIAL ATTACHMENT." International Journal of Bifurcation and Chaos 20, no. 03 (2010): 923–27. http://dx.doi.org/10.1142/s0218127410026216.

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In this paper, we present results concerning a natural extension of the class of heterogeneous preferential attachment models, a generalization of the Barabási–Albert model to heterogeneous networks. In this extended class, the network nodes enjoy a nonzero attractiveness even when their connectivity degrees are zero. We analytically show that the degree densities of models in the extended class exhibit a richer scaling behavior than their homogeneous counterparts, and that power-law scaling in their degree distribution is robust in the presence of the offset in the attachment kernel.

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40

Poncela, Julia, Jesús Gómez-Gardeñes, Luis M. Floría, Angel Sánchez, and Yamir Moreno. "Complex Cooperative Networks from Evolutionary Preferential Attachment." PLoS ONE 3, no. 6 (2008): e2449. http://dx.doi.org/10.1371/journal.pone.0002449.

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41

Yudina, M. N. "Preferential attachment graphs as agent interaction structure." Journal of Physics: Conference Series 1441 (January 2020): 012176. http://dx.doi.org/10.1088/1742-6596/1441/1/012176.

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42

Jordan, Jonathan. "Degree sequences of geometric preferential attachment graphs." Advances in Applied Probability 42, no. 02 (2010): 319–30. http://dx.doi.org/10.1017/s0001867800004079.

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We investigate the degree sequence of the geometric preferential attachment model of Flaxman, Frieze and Vera (2006), (2007) in the case where the self-loop parameter α is set to 0. We show that, given certain conditions on the attractiveness function F, the degree sequence converges to the same sequence as found for standard preferential attachment in Bollobás et al. (2001). We also apply our method to the extended model introduced in van der Esker (2008) which allows for an initial attractiveness term, proving similar results.

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43

Wang, Li-Na, Jin-Li Guo, Han-Xin Yang, and Tao Zhou. "Local preferential attachment model for hierarchical networks." Physica A: Statistical Mechanics and its Applications 388, no. 8 (2009): 1713–20. http://dx.doi.org/10.1016/j.physa.2008.12.028.

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44

Chrysafis, O., and C. Cannings. "Weighted self-similar networks under preferential attachment." Physica A: Statistical Mechanics and its Applications 388, no. 14 (2009): 2965–74. http://dx.doi.org/10.1016/j.physa.2009.03.030.

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45

Dai, Meifeng, Qi Xie, and Lei Li. "Dynamic weight evolution network with preferential attachment." Physica A: Statistical Mechanics and its Applications 416 (December 2014): 149–56. http://dx.doi.org/10.1016/j.physa.2014.08.049.

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46

Lyon, Merritt R., and Hosam M. Mahmoud. "Trees grown under young-age preferential attachment." Journal of Applied Probability 57, no. 3 (2020): 911–27. http://dx.doi.org/10.1017/jpr.2020.49.

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AbstractWe introduce a class of non-uniform random recursive trees grown with an attachment preference for young age. Via the Chen–Stein method of Poisson approximation, we find that the outdegree of a node is characterized in the limit by ‘perturbed’ Poisson laws, and the perturbation diminishes as the node index increases. As the perturbation is attenuated, a pure Poisson limit ultimately emerges in later phases. Moreover, we derive asymptotics for the proportion of leaves and show that the limiting fraction is less than one half. Finally, we study the insertion depth in a random tree in thi

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47

Ruiz, Diego, Juan Campos, and Jorge Finke. "Dynamics in Affinity-Weighted Preferential Attachment Networks." Journal of Statistical Physics 181, no. 2 (2020): 673–89. http://dx.doi.org/10.1007/s10955-020-02594-0.

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48

Flaxman, Abraham D., Alan M. Frieze, and Juan Vera. "A Geometric Preferential Attachment Model of Networks." Internet Mathematics 3, no. 2 (2006): 187–205. http://dx.doi.org/10.1080/15427951.2006.10129124.

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49

Malyshkin, Y. A. "Preferential attachment with choice-based edge step." Statistics & Probability Letters 224 (September 2025): 110438. https://doi.org/10.1016/j.spl.2025.110438.

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50

Siu, Chunyin. "The Topological Behavior of Preferential Attachment Graphs." SIAM Journal on Applied Algebra and Geometry 9, no. 2 (2025): 432–55. https://doi.org/10.1137/24m1693179.

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