Spectral plot properties: Towards a qualitative classification of networks

  • Received: 01 August 2007 Revised: 01 February 2008
  • Primary: 05C75, 47A75; Secondary: 90C35, 68R10.

  • We introduce a tentative classification scheme for empirical networks based on global qualitative properties detected through the spectrum of the Laplacian of the graph underlying the network. Our method identifies several distinct types of networks across different domains of applications, indicates hidden regularity properties and provides evidence for processes like node duplication behind the evolution or construction of a given class of networks.

    Citation: Anirban Banerjee, Jürgen Jost. Spectral plot properties: Towards a qualitative classification of networks[J]. Networks and Heterogeneous Media, 2008, 3(2): 395-411. doi: 10.3934/nhm.2008.3.395

    Related Papers:

    [1] Anirban Banerjee, Jürgen Jost . Spectral plot properties: Towards a qualitative classification of networks. Networks and Heterogeneous Media, 2008, 3(2): 395-411. doi: 10.3934/nhm.2008.3.395
    [2] Robert Carlson . Spectral theory for nonconservative transmission line networks. Networks and Heterogeneous Media, 2011, 6(2): 257-277. doi: 10.3934/nhm.2011.6.257
    [3] Joachim von Below, José A. Lubary . Isospectral infinite graphs and networks and infinite eigenvalue multiplicities. Networks and Heterogeneous Media, 2009, 4(3): 453-468. doi: 10.3934/nhm.2009.4.453
    [4] Yunhua Liao, Mohamed Maama, M. A. Aziz-Alaoui . Consensus dynamics and coherence in hierarchical small-world networks. Networks and Heterogeneous Media, 2025, 20(2): 482-499. doi: 10.3934/nhm.2025022
    [5] Mirela Domijan, Markus Kirkilionis . Graph theory and qualitative analysis of reaction networks. Networks and Heterogeneous Media, 2008, 3(2): 295-322. doi: 10.3934/nhm.2008.3.295
    [6] Regino Criado, Julio Flores, Alejandro J. García del Amo, Miguel Romance . Structural properties of the line-graphs associated to directed networks. Networks and Heterogeneous Media, 2012, 7(3): 373-384. doi: 10.3934/nhm.2012.7.373
    [7] M. D. König, Stefano Battiston, M. Napoletano, F. Schweitzer . On algebraic graph theory and the dynamics of innovation networks. Networks and Heterogeneous Media, 2008, 3(2): 201-219. doi: 10.3934/nhm.2008.3.201
    [8] Zhong-Jie Han, Gen-Qi Xu . Spectrum and dynamical behavior of a kind of planar network of non-uniform strings with non-collocated feedbacks. Networks and Heterogeneous Media, 2010, 5(2): 315-334. doi: 10.3934/nhm.2010.5.315
    [9] Raúl M. Falcón, Venkitachalam Aparna, Nagaraj Mohanapriya . Optimal secret share distribution in degree splitting communication networks. Networks and Heterogeneous Media, 2023, 18(4): 1713-1746. doi: 10.3934/nhm.2023075
    [10] Jan Haskovec, Vybíral Jan . Robust network formation with biological applications. Networks and Heterogeneous Media, 2024, 19(2): 771-799. doi: 10.3934/nhm.2024035
  • We introduce a tentative classification scheme for empirical networks based on global qualitative properties detected through the spectrum of the Laplacian of the graph underlying the network. Our method identifies several distinct types of networks across different domains of applications, indicates hidden regularity properties and provides evidence for processes like node duplication behind the evolution or construction of a given class of networks.


  • This article has been cited by:

    1. Jürgen Jost, 2009, Chapter 4, 978-3-642-00615-9, 51, 10.1007/978-3-642-00616-6_4
    2. Bing Wang, Zhiwen Sun, Yuexing Han, A Path-Based Distribution Measure for Network Comparison, 2020, 22, 1099-4300, 1287, 10.3390/e22111287
    3. Sarika Jalan, Spectral analysis of deformed random networks, 2009, 80, 1539-3755, 10.1103/PhysRevE.80.046101
    4. Jiao Gu, Bobo Hua, Shiping Liu, Spectral distances on graphs, 2015, 190-191, 0166218X, 56, 10.1016/j.dam.2015.04.011
    5. Francesc Comellas, Jordi Diaz-Lopez, Spectral reconstruction of complex networks, 2008, 387, 03784371, 6436, 10.1016/j.physa.2008.07.032
    6. Anirban Banerjee, Structural distance and evolutionary relationship of networks, 2012, 107, 03032647, 186, 10.1016/j.biosystems.2011.11.004
    7. Ankit Agrawal, Camellia Sarkar, Sanjiv K. Dwivedi, Nitesh Dhasmana, Sarika Jalan, Quantifying randomness in protein–protein interaction networks of different species: A random matrix approach, 2014, 404, 03784371, 359, 10.1016/j.physa.2013.12.005
    8. Saurabh Sawlani, Lingxiao Zhao, Leman Akoglu, 2021, Fast Attributed Graph Embedding via Density of States, 978-1-6654-2398-4, 559, 10.1109/ICDM51629.2021.00067
    9. Claire Donnat, Susan Holmes, Tracking network dynamics: A survey using graph distances, 2018, 12, 1932-6157, 10.1214/18-AOAS1176
    10. Melanie Weber, Emil Saucan, Jürgen Jost, Characterizing complex networks with Forman-Ricci curvature and associated geometric flows, 2017, 5, 2051-1310, 527, 10.1093/comnet/cnw030
    11. Anirban Banerjee, Jürgen Jost, 2009, Chapter 7, 978-0-8176-4750-6, 117, 10.1007/978-0-8176-4751-3_7
    12. Lingxiao Zhao, Saurabh Sawlani, Leman Akoglu, Density of states for fast embedding node-attributed graphs, 2023, 0219-1377, 10.1007/s10115-023-01836-3
    13. Melanie Weber, Jürgen Jost, Emil Saucan, Forman-Ricci Flow for Change Detection in Large Dynamic Data Sets, 2016, 5, 2075-1680, 26, 10.3390/axioms5040026
    14. Jiao Gu, Jürgen Jost, Shiping Liu, Peter F. Stadler, Spectral classes of regular, random, and empirical graphs, 2016, 489, 00243795, 30, 10.1016/j.laa.2015.08.038
    15. Leonardo Scabini, Lucas Ribas, Eraldo Ribeiro, Odemir Bruno, 2022, Chapter 5, 978-3-030-97239-4, 54, 10.1007/978-3-030-97240-0_5
    16. Krishanu Deyasi, Anirban Banerjee, Bony Deb, Phylogeny of metabolic networks: A spectral graph theoretical approach, 2015, 40, 0250-5991, 799, 10.1007/s12038-015-9562-0
    17. Melanie Weber, Emil Saucan, Jürgen Jost, Ernesto Estrada, Coarse geometry of evolving networks, 2018, 6, 2051-1310, 706, 10.1093/comnet/cnx049
    18. Jeaneth Machicao, Francesco Craighero, Davide Maspero, Fabrizio Angaroni, Chiara Damiani , Alex Graudenzi , Marco Antoniotti, Odemir M. Bruno, On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples, 2021, 22, 13892029, 88, 10.2174/1389202922666210301084151
    19. Hao Chen, Jürgen Jost, Minimum vertex covers and the spectrum of the normalized Laplacian on trees, 2012, 437, 00243795, 1089, 10.1016/j.laa.2012.04.005
    20. J Gu, Y Zhu, L Guo, J Jiang, L Chi, W Li, Q A Wang, X Cai, Recent Progress in Some Active Topics on Complex Networks, 2015, 604, 1742-6596, 012007, 10.1088/1742-6596/604/1/012007
    21. Jürgen Jost, Kiran M. Kolwankar, Evolution of network structure by temporal learning, 2009, 388, 03784371, 1959, 10.1016/j.physa.2008.12.073
    22. Lucas C. Ribas, Jeaneth Machicao, Odemir M. Bruno, Life-Like Network Automata descriptor based on binary patterns for network classification, 2020, 515, 00200255, 156, 10.1016/j.ins.2019.09.063
    23. B. Tadić, M. Mitrović, Jamming and correlation patterns in traffic of information on sparse modular networks, 2009, 71, 1434-6028, 631, 10.1140/epjb/e2009-00190-7
    24. Marija Mitrović, Bosiljka Tadić, Spectral and dynamical properties in classes of sparse networks with mesoscopic inhomogeneities, 2009, 80, 1539-3755, 10.1103/PhysRevE.80.026123
    25. Raffaella Mulas, Danijela Horak, Jürgen Jost, 2022, Chapter 1, 978-3-030-91373-1, 1, 10.1007/978-3-030-91374-8_1
    26. Jürgen Jost, Raffaella Mulas, Ernesto Estrada, Normalized Laplace operators for hypergraphs with real coefficients, 2021, 9, 2051-1310, 10.1093/comnet/cnab009
  • Reader Comments
  • © 2008 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4401) PDF downloads(136) Cited by(26)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog