Detecting the core of a network by the centralities of the nodes

doi:10.1088/1674-1056/ad4cd4

中国物理B ›› 2024, Vol. 33 ›› Issue (8): 88903-088903.doi: 10.1088/1674-1056/ad4cd4

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Detecting the core of a network by the centralities of the nodes

Peijie Ma(马佩杰), Xuezao Ren(任学藻)†, Junfang Zhu(朱军芳)‡, and Yanqun Jiang(蒋艳群)

School of Mathematics and Science, Southwest University of Science and Technology, Mianyang 621010, China

收稿日期:2024-01-23 修回日期:2024-04-15 出版日期:2024-08-15 发布日期:2024-07-15
通讯作者: Xuezao Ren, Junfang Zhu E-mail:rxz63@aliyun.com;zjfbird@mail.ustc.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Gant No. 11872323).

Detecting the core of a network by the centralities of the nodes

Peijie Ma(马佩杰), Xuezao Ren(任学藻)†, Junfang Zhu(朱军芳)‡, and Yanqun Jiang(蒋艳群)

School of Mathematics and Science, Southwest University of Science and Technology, Mianyang 621010, China

Received:2024-01-23 Revised:2024-04-15 Online:2024-08-15 Published:2024-07-15
Contact: Xuezao Ren, Junfang Zhu E-mail:rxz63@aliyun.com;zjfbird@mail.ustc.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Gant No. 11872323).

摘要/Abstract

摘要： Many networks exhibit the core/periphery structure. Core/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral nodes. Core nodes tend to be well-connected, both among themselves and to peripheral nodes, which tend not to be well-connected to other nodes. In this brief report, we propose a new method to detect the core of a network by the centrality of each node. It is discovered that such nodes with non-negative centralities often consist in the core of the networks. The simulation is carried out on different real networks. The results are checked by the objective function. The checked results may show the effectiveness of the simulation results by the centralities of the nodes on the real networks. Furthermore, we discuss the characters of networks with the single core/periphery structure and point out the scope of the application of our method at the end of this paper.

关键词: complex network, core/periphery structure, the objective function

Abstract: Many networks exhibit the core/periphery structure. Core/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral nodes. Core nodes tend to be well-connected, both among themselves and to peripheral nodes, which tend not to be well-connected to other nodes. In this brief report, we propose a new method to detect the core of a network by the centrality of each node. It is discovered that such nodes with non-negative centralities often consist in the core of the networks. The simulation is carried out on different real networks. The results are checked by the objective function. The checked results may show the effectiveness of the simulation results by the centralities of the nodes on the real networks. Furthermore, we discuss the characters of networks with the single core/periphery structure and point out the scope of the application of our method at the end of this paper.

Key words: complex network, core/periphery structure, the objective function

中图分类号: (Networks and genealogical trees)

89.75.Hc

Peijie Ma(马佩杰), Xuezao Ren(任学藻), Junfang Zhu(朱军芳), and Yanqun Jiang(蒋艳群). Detecting the core of a network by the centralities of the nodes[J]. 中国物理B, 2024, 33(8): 88903-088903.

Peijie Ma(马佩杰), Xuezao Ren(任学藻), Junfang Zhu(朱军芳), and Yanqun Jiang(蒋艳群). Detecting the core of a network by the centralities of the nodes[J]. Chin. Phys. B, 2024, 33(8): 88903-088903.

参考文献

[1] Csermely P, London A, Wu L Y and Uzzi B 2013 Journal of Complex Networks 1 93
[2] Borgatti S P and Everett M G 1999 Social Networks 21 375
[3] Holme P 2005 Phys. Rev. E 72 046111
[4] Da Silva M R, Ma H and Zeng A P 2008 Proc. IEEE 96 1411
[5] Ma C, Yang X L, Chen H S and Zhang H F 2021 Acta Phys. Sin. 70 228901 (in Chinese)
[6] Liu Y Y, Slotine J J and Barabási A L 2011 Nature 473 167
[7] Rombach M P, Porter M A, Fowler J H and Mucha P J 2014 SIAM Journal on Applied Mathematics 74 167
[8] Della Rossa F, Dercole F and Piccardi C 2013 Scientific Reports 3 1467
[9] Shen X, Han Y, Li W Q, Wong K C and Peng C B 2021 Physica A 581 126224
[10] Colizza V, Flammini A, Serrano M A and Vespignani A 2006 Nature 2 110
[11] Xu X K, Zhang J and Small M 2010 Phys. Rev. E 82 046117
[12] Khanday A M U D, Wani M A, Rabani S T and Khan Q R 2023 Sustainability 15 1249
[13] Polanco A and Newman M E J 2023 Phys. Rev. E 108 024311
[14] Towlson E K, Vértes P E, Ahnert S E, Schafer W R and Bullmore E T 2013 The Journal of Neuroscience 33 6380
[15] Mondragón R J 2014 Journal of Complex Networks 2 288
[16] Yan J, Anghinoni L, Zhu Y T, Liu W, Li G, Zheng Q and Zhao L 2023 Journal of Computational Science 66 101912
[17] Masuda N and Konno N 2006 Social Networks 28 297
[18] Ball G, Aljabar P, Zebari S, Tusor N, Arichi T, Merchant N, Robinson E, Ogundipe E, Rueckert D, Edwards A and Counsell S 2014 Proc. Natl. Acad. Sci. USA 111 7456
[19] Grayson D S, Ray S, Carpenter S, Iyer S, Dias T G C, Stevens C, Nigg J T and Fair D A 2014 PloS One 9 e88297
[20] Lee S H, Cucuringu M and Porter M A 2014 Phys. Rev. E 89 032810
[21] Shen X, Aliko S, Han Y, Skipper J I and Peng C 2021 IEEE Transactions on Network Science and Engineering 9 875
[22] Ai D, Liu X L, Kang W Z, Li L N, Lü S Q and Liu Y 2023 Chin. Phys. B 32 118902
[23] Chen D, Cai D and Su H 2023 Chin. Phys. B 32 098903
[24] Zachary W W 1977 Journal of anthropological research 33 452
[25] Cucringu M, Rombach P, Lee S H and Porter M A 2016 European Journal of Applied Mathematics 27 846
[26] van Lidth de Jeude J, Caldarelli G and Squartini T 2019 Europhysics Letters 125 68001
[27] Kojaku S and Masuda N 2017 Phys. Rev. E 96 052313
[28] Lusseau D 2003 Proc. R. Soc. Lond. B 270 186
[29] Newman M E J 2006 Phys. Rev. E 74 036104
[30] Adamic L A and Glance N 2005 Proceedings of the 3rd International Workshop on Link Discovery, August 21, 2005, New York, USA, p. 36
[31] Xiang B B, Bao Z K, Ma C, Zhang X, Chen H S and Zhang H F 2018 Chaos 28 013122
[32] Girvan M and Newman M E J 2002 Proc. Natl. Acad. Sci. USA 99 7821

Detecting the core of a network by the centralities of the nodes

Detecting the core of a network by the centralities of the nodes

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