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K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases

  • Received: 01 February 2008 Revised: 01 March 2008
  • 68R10, 05C90, 68M07.

  • We consider the k-core decomposition of network models and Internet graphs at the autonomous system (AS) level. The k-core analysis allows to characterize networks beyond the degree distribution and uncover structural properties and hierarchies due to the specific architecture of the system. We compare the k-core structure obtained for AS graphs with those of several network models and discuss the differences and similarities with the real Internet architecture. The presence of biases and the incompleteness of the real maps are discussed and their effect on the k-core analysis is assessed with numerical experiments simulating biased exploration on a wide range of network models. We find that the k-core analysis provides an interesting characterization of the fluctuations and incompleteness of maps as well as information helping to discriminate the original underlying structure.

    Citation: José Ignacio Alvarez-Hamelin, Luca Dall'Asta, Alain Barrat, Alessandro Vespignani. K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases[J]. Networks and Heterogeneous Media, 2008, 3(2): 371-393. doi: 10.3934/nhm.2008.3.371

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  • We consider the k-core decomposition of network models and Internet graphs at the autonomous system (AS) level. The k-core analysis allows to characterize networks beyond the degree distribution and uncover structural properties and hierarchies due to the specific architecture of the system. We compare the k-core structure obtained for AS graphs with those of several network models and discuss the differences and similarities with the real Internet architecture. The presence of biases and the incompleteness of the real maps are discussed and their effect on the k-core analysis is assessed with numerical experiments simulating biased exploration on a wide range of network models. We find that the k-core analysis provides an interesting characterization of the fluctuations and incompleteness of maps as well as information helping to discriminate the original underlying structure.


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    13. D. Fay, H. Haddadi, A. Thomason, A.W. Moore, R. Mortier, A. Jamakovic, S. Uhlig, M. Rio, Weighted Spectral Distribution for Internet Topology Analysis: Theory and Applications, 2010, 18, 1063-6692, 164, 10.1109/TNET.2009.2022369
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    19. Fabio Della Rossa, Fabio Dercole, Carlo Piccardi, Profiling core-periphery network structure by random walkers, 2013, 3, 2045-2322, 10.1038/srep01467
    20. Ying Zhang, Lu Qin, Fan Zhang, Wenjie Zhang, 2019, Hierarchical Decomposition of Big Graphs, 978-1-5386-7474-1, 2064, 10.1109/ICDE.2019.00240
    21. Abhijin Adiga, Anil Kumar S. Vullikanti, 2013, Chapter 35, 978-3-642-38708-1, 541, 10.1007/978-3-642-40988-2_35
    22. Matthew T. Carlson, Morgan Sonderegger, Max Bane, How children explore the phonological network in child-directed speech: A survival analysis of children’s first word productions, 2014, 75, 0749596X, 159, 10.1016/j.jml.2014.05.005
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    24. 2016, 9780124079083, 433, 10.1016/B978-0-12-407908-3.10000-7
    25. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi, 2019, Chapter 4, 978-3-030-10766-6, 91, 10.1007/978-3-030-10767-3_4
    26. Carlo Lipizzi, Dante Gama Dessavre, Luca Iandoli, Jose Emmanuel Ramirez Marquez, Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations, 2016, 64, 07475632, 782, 10.1016/j.chb.2016.07.030
    27. Qi Luo, Dongxiao Yu, Zhipeng Cai, Xuemin Lin, Guanghui Wang, Xiuzhen Cheng, Toward maintenance of hypercores in large-scale dynamic hypergraphs, 2022, 1066-8888, 10.1007/s00778-022-00763-z
    28. Ying Liu, Ming Tang, Tao Zhou, , Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition, 2015, 5, 2045-2322, 10.1038/srep09602
    29. Tao Zhao, M Eric Schranz, Network approaches for plant phylogenomic synteny analysis, 2017, 36, 13695266, 129, 10.1016/j.pbi.2017.03.001
    30. S. Yoon, A. V. Goltsev, J. F. F. Mendes, Structural stability of interaction networks against negative external fields, 2018, 97, 2470-0045, 10.1103/PhysRevE.97.042311
    31. Deming Chu, Fan Zhang, Wenjie Zhang, Xuemin Lin, Ying Zhang, 2022, Hierarchical Core Decomposition in Parallel: From Construction to Subgraph Search, 978-1-6654-0883-7, 1138, 10.1109/ICDE53745.2022.00090
    32. Francesco Bonchi, Francesco Gullo, Andreas Kaltenbrunner, 2018, Chapter 110176, 978-1-4939-7130-5, 419, 10.1007/978-1-4939-7131-2_110176
    33. Feng Zhao, Anthony K. H. Tung, Large scale cohesive subgraphs discovery for social network visual analysis, 2012, 6, 2150-8097, 85, 10.14778/2535568.2448942
    34. Motoki Kajiwara, Ritsuki Nomura, Felix Goetze, Masanori Kawabata, Yoshikazu Isomura, Tatsuya Akutsu, Masanori Shimono, Daniele Marinazzo, Inhibitory neurons exhibit high controlling ability in the cortical microconnectome, 2021, 17, 1553-7358, e1008846, 10.1371/journal.pcbi.1008846
    35. An Zeng, Cheng-Jun Zhang, Ranking spreaders by decomposing complex networks, 2013, 377, 03759601, 1031, 10.1016/j.physleta.2013.02.039
    36. Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney, 2013, Tree-Like Structure in Large Social and Information Networks, 978-0-7695-5108-1, 1, 10.1109/ICDM.2013.77
    37. Giridhar Maji, Influential spreaders identification in complex networks with potential edge weight based k-shell degree neighborhood method, 2020, 39, 18777503, 101055, 10.1016/j.jocs.2019.101055
    38. J. Garcia-Algarra, J. M. Pastor, M. L. Mouronte, J. Galeano, A Structural Approach to Disentangle the Visualization of Bipartite Biological Networks, 2018, 2018, 1076-2787, 1, 10.1155/2018/6204947
    39. Weifeng Pan, Bing Li, Jing Liu, Yutao Ma, Bo Hu, Analyzing the structure of Java software systems by weightedK-core decomposition, 2018, 83, 0167739X, 431, 10.1016/j.future.2017.09.039
    40. Glencora Borradaile, Theresa Migler, Gordon Wilfong, 2018, Chapter 2, 978-3-319-73197-1, 15, 10.1007/978-3-319-73198-8_2
    41. Miloš Savić, Mirjana Ivanović, Lakhmi C. Jain, 2019, Chapter 2, 978-3-319-91194-6, 17, 10.1007/978-3-319-91196-0_2
    42. Beatrice Crona, Stefan Gelcich, Örjan Bodin, The Importance of Interplay Between Leadership and Social Capital in Shaping Outcomes of Rights-Based Fisheries Governance, 2017, 91, 0305750X, 70, 10.1016/j.worlddev.2016.10.006
    43. J. Ignacio Alvarez-Hamelin, Mariano G. Beiró, Jorge R. Busch, Understanding Edge Connectivity in the Internet through Core Decomposition, 2011, 7, 1542-7951, 45, 10.1080/15427951.2011.560786
    44. Javier Borge-Holthoefer, Yamir Moreno, Absence of influential spreaders in rumor dynamics, 2012, 85, 1539-3755, 10.1103/PhysRevE.85.026116
    45. Omri Ben-Eliezer, Talya Eden, Joel Oren, Dimitris Fotakis, 2022, Sampling Multiple Nodes in Large Networks, 9781450391320, 37, 10.1145/3488560.3498383
    46. Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney, Tree decompositions and social graphs, 2016, 12, 1542-7951, 315, 10.1080/15427951.2016.1182952
    47. Javier Borge-Holthoefer, Sandra González-Bailón, Alejandro Rivero, Yamir Moreno, 2014, Chapter 9, 978-3-7091-1339-4, 155, 10.1007/978-3-7091-1340-0_9
    48. Qing Liu, Xuliang Zhu, Xin Huang, Jianliang Xu, Local algorithms for distance-generalized core decomposition over large dynamic graphs, 2021, 14, 2150-8097, 1531, 10.14778/3461535.3461542
    49. Kijung Shin, Tina Eliassi-Rad, Christos Faloutsos, 2016, CoreScope: Graph Mining Using k-Core Analysis — Patterns, Anomalies and Algorithms, 978-1-5090-5473-2, 469, 10.1109/ICDM.2016.0058
    50. G. J. Baxter, S. N. Dorogovtsev, A. V. Goltsev, J. F. F. Mendes, Heterogeneousk-core versus bootstrap percolation on complex networks, 2011, 83, 1539-3755, 10.1103/PhysRevE.83.051134
    51. Dongxiao Yu, Na Wang, Qi Luo, Feng Li, Jiguo Yu, Xiuzhen Cheng, Zhipeng Cai, Fast Core Maintenance in Dynamic Graphs, 2022, 9, 2329-924X, 710, 10.1109/TCSS.2021.3064836
    52. Hidayet Aksu, Mustafa Canim, Yuan-Chi Chang, Ibrahim Korpeoglu, ozgur Ulusoy, Distributed k -Core View Materialization and Maintenance for Large Dynamic Graphs, 2014, 26, 1041-4347, 2439, 10.1109/TKDE.2013.2297918
    53. Zhe Lin, Fan Zhang, Xuemin Lin, Wenjie Zhang, Zhihong Tian, Hierarchical core maintenance on large dynamic graphs, 2021, 14, 2150-8097, 757, 10.14778/3446095.3446099
    54. Enrico Gregori, Luciano Lenzini, Chiara Orsini, 2011, k-clique Communities in the Internet AS-level Topology Graph, 978-1-4577-0384-3, 134, 10.1109/ICDCSW.2011.17
    55. 2016, Evolution and characteristics of the Internet Nucleus, 978-1-4673-6679-3, 1, 10.1109/ICCCI.2016.7480012
    56. Flaviano Morone, Kate Burleson-Lesser, H.A. Vinutha, Srikanth Sastry, Hernán A. Makse, The jamming transition is a k-core percolation transition, 2019, 516, 03784371, 172, 10.1016/j.physa.2018.10.035
    57. WEIFENG PAN, BO HU, JILEI DONG, KUN LIU, BO JIANG, STRUCTURAL PROPERTIES OF MULTILAYER SOFTWARE NETWORKS: A CASE STUDY IN TOMCAT, 2018, 21, 0219-5259, 1850004, 10.1142/S0219525918500042
    58. G. J. Baxter, S. N. Dorogovtsev, K.-E. Lee, J. F. F. Mendes, A. V. Goltsev, Critical Dynamics of thek-Core Pruning Process, 2015, 5, 2160-3308, 10.1103/PhysRevX.5.031017
    59. Alexander Mehler, Tim vor der Brück, Rüdiger Gleim, T. Geelhaar, 2014, Chapter 5, 978-3-319-12654-8, 87, 10.1007/978-3-319-12655-5_5
    60. Wen Bai, Yuxiao Zhang, Xuezheng Liu, Min Chen, Di Wu, 2020, Chapter 42, 978-3-030-59415-2, 658, 10.1007/978-3-030-59416-9_42
    61. Hamed Haddadi, Damien Fay, Steve Uhlig, Andrew Moore, Richard Mortier, Almerima Jamakovic, 2010, Chapter 3, 978-3-642-12364-1, 32, 10.1007/978-3-642-12365-8_3
    62. Rong-Hua Li, Jeffrey Xu Yu, Rui Mao, Efficient Core Maintenance in Large Dynamic Graphs, 2014, 26, 1041-4347, 2453, 10.1109/TKDE.2013.158
    63. Vipin Narang, Muhamad Azfar Ramli, Amit Singhal, Pavanish Kumar, Gennaro de Libero, Michael Poidinger, Christopher Monterola, Michael A Beer, Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks, 2015, 11, 1553-7358, e1004504, 10.1371/journal.pcbi.1004504
    64. Jinho Lee, Constructing a Social Contact Network based on Cellphone Call Records and Analysis of its Scale-free Property, 2014, 40, 1225-0988, 1, 10.7232/JKIIE.2014.40.1.001
    65. Scott Kirkpatrick, Alex Kulakovsky, Manuel Cebrian, Alex “Sandy” Pentland, Social networks and spin glasses, 2012, 92, 1478-6435, 362, 10.1080/14786435.2011.634858
    66. Kijung Shin, Tina Eliassi-Rad, Christos Faloutsos, Patterns and anomalies in k-cores of real-world graphs with applications, 2018, 54, 0219-1377, 677, 10.1007/s10115-017-1077-6
    67. Alberto Garcia-Robledo, Arturo Diaz-Perez, Guillermo Morales-Luna, 2015, Exploring the feasibility of heterogeneous computing of complex networks for big data analysis, 978-1-4673-7865-9, 1, 10.1109/CEWIT.2015.7338160
    68. Hui Li, Rong Chen, Xin Ge, Li-Ying Hao, Hai Zhao, Extraction and Analysis of Crucial Fraction in Software Networks, 2014, 24, 0218-1940, 617, 10.1142/S0218194014500235
    69. Xin Du, Weifeng Pan, Bo Jiang, Luyun Ding, Yun Pan, Chengxiang Yuan, Yiming Xiang, CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation, 2022, 11, 2075-1680, 491, 10.3390/axioms11100491
    70. Jun Zhang, Hai Zhao, Jiuqiang Xu, Zheng Liu, Characterizing and modeling the Internet Router-level topology – The hierarchical features and HIR model, 2010, 33, 01403664, 2001, 10.1016/j.comcom.2010.07.010
    71. Ming Gao, Ee-Peng Lim, David Lo, Philips Kokoh Prasetyo, On detecting maximal quasi antagonistic communities in signed graphs, 2016, 30, 1384-5810, 99, 10.1007/s10618-015-0405-2
    72. Francesca Arese Lucini, Gino Del Ferraro, Mariano Sigman, Hernán A. Makse, How the Brain Transitions from Conscious to Subliminal Perception, 2019, 411, 03064522, 280, 10.1016/j.neuroscience.2019.03.047
    73. Long Yuan, Lu Qin, Xuemin Lin, Lijun Chang, Wenjie Zhang, I/O efficient ECC graph decomposition via graph reduction, 2017, 26, 1066-8888, 275, 10.1007/s00778-016-0451-4
    74. S. N. Dorogovtsev, A. V. Goltsev, J. F. F. Mendes, Critical phenomena in complex networks, 2008, 80, 0034-6861, 1275, 10.1103/RevModPhys.80.1275
    75. Irene Malvestio, Alessio Cardillo, Naoki Masuda, Interplay between
    k
    -core and community structure in complex networks, 2020, 10, 2045-2322, 10.1038/s41598-020-71426-8
    76. Ahmet Erdem Sariyüce, Ali Pinar, Fast hierarchy construction for dense subgraphs, 2016, 10, 2150-8097, 97, 10.14778/3021924.3021927
    77. Sabeur Aridhi, Alberto Montresor, Yannis Velegrakis, 2016, BLADYG, 9781450343503, 39, 10.1145/2915516.2915525
    78. Jin-Long Liu, Zu-Guo Yu, Vo Anh, Multifractal analysis for core-periphery structure of complex networks, 2019, 2019, 1742-5468, 073405, 10.1088/1742-5468/ab2906
    79. Qin Gao, Chao Sun, Chunyan Yang, The Influence of Network Structural Properties on Information Dissemination Power in Microblogging Systems, 2014, 30, 1044-7318, 394, 10.1080/10447318.2013.873279
    80. Brigitte Gay, How do distinct firm assets and behaviors shape the form of alliance networks and provoke their instability? A multi-level network analysis, 2015, n°16, 2032-5355, 73, 10.3917/jie.016.0073
    81. Xuelian He, Zhenhuan Liu, Chia-Huei Wu, Discussion on the Construction of Interactive Chinese Teaching Mode of Mobile App Application under the Internet Background, 2022, 2022, 1875-905X, 1, 10.1155/2022/1820946
    82. Srinka Basu, Ujjwal Maulik, A Game Theory Inspired Approach to Stable Core Decomposition on Weighted Networks, 2016, 28, 1041-4347, 1105, 10.1109/TKDE.2015.2508817
    83. Maksim Kitsak, Massimo Riccaboni, Shlomo Havlin, Fabio Pammolli, H. Eugene Stanley, Scale-free models for the structure of business firm networks, 2010, 81, 1539-3755, 10.1103/PhysRevE.81.036117
    84. Yingmei Wei, Xiaolei Du, 2017, Two-Layer Network Visualization for Comprehensive Analysis, 978-1-5386-1600-0, 363, 10.1109/DSC.2017.99
    85. Fan Zhang, Jiadong Xie, Kai Wang, Shiyu Yang, Yu Jiang, Discovering key users for defending network structural stability, 2022, 25, 1386-145X, 679, 10.1007/s11280-021-00905-3
    86. Zhongxiao Hu, 2021, Construction of Labor Education System in Applied Undergraduate Colleges under the Background of Internet Education Based on Information Technology, 9781450390255, 915, 10.1145/3482632.3483050
    87. Jingzhi Tu, Gang Mei, Francesco Piccialli, An improved Nyström spectral graph clustering using k-core decomposition as a sampling strategy for large networks, 2022, 34, 13191578, 3673, 10.1016/j.jksuci.2022.04.009
    88. Ying Liu, Ming Tang, Tao Zhou, Younghae Do, Improving the accuracy of the k-shell method by removing redundant links: From a perspective of spreading dynamics, 2015, 5, 2045-2322, 10.1038/srep13172
    89. Carlo Drago, The Analysis of the Core-Periphery Structure of Large Networks Combining Interval Value-Data Regression Approaches, 2018, 1556-5068, 10.2139/ssrn.3277345
    90. E Gregori, L Lenzini, C Orsini, 2011, k-dense communities in the internet AS-level topology, 978-1-4244-8952-7, 1, 10.1109/COMSNETS.2011.5716413
    91. 2018, chapter 2, 9781522537991, 30, 10.4018/978-1-5225-3799-1.ch002
    92. Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, Nicola Santoro, Time-varying graphs and dynamic networks, 2012, 27, 1744-5760, 387, 10.1080/17445760.2012.668546
    93. Brigitte Gay, 2020, Chapter 423, 978-3-319-15346-9, 1735, 10.1007/978-3-319-15347-6_423
    94. CHRIS COOPER, NOEL SCOTT, RODOLFO BAGGIO, Network Position and Perceptions of Destination Stakeholder Importance, 2009, 20, 1303-2917, 33, 10.1080/13032917.2009.10518893
    95. Chengcheng Shao, Pik-Mai Hui, Lei Wang, Xinwen Jiang, Alessandro Flammini, Filippo Menczer, Giovanni Luca Ciampaglia, Alain Barrat, Anatomy of an online misinformation network, 2018, 13, 1932-6203, e0196087, 10.1371/journal.pone.0196087
    96. Aman Ullah, Bin wang, Jinfang Sheng, Jun Long, Nasrullah Khan, Lucia Valentina Gambuzza, Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks, 2021, 2021, 1099-0526, 1, 10.1155/2021/8403738
    97. Brigitte Gay, 2013, Chapter 423, 978-1-4614-3857-1, 1359, 10.1007/978-1-4614-3858-8_423
    98. Lihong Zhen, Linkage development of sports industry and mobile internet in the internet era, 2021, 10641246, 1, 10.3233/JIFS-219103
    99. Esteban Carisimo, Carlos Selmo, J. Ignacio Alvarez-Hamelin, Amogh Dhamdhere, 2018, Studying the Evolution of Content Providers in the Internet Core, 978-3-903176-09-6, 1, 10.23919/TMA.2018.8506513
    100. Zhichao Ba, Yujie Cao, Jin Mao, Gang Li, A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science, 2019, 119, 0138-9130, 1455, 10.1007/s11192-019-03103-1
    101. Jason Samuel Sherwin, Jordan Muraskin, Paul Sajda, Pre-stimulus functional networks modulate task performance in time-pressured evidence gathering and decision-making, 2015, 111, 10538119, 513, 10.1016/j.neuroimage.2015.01.023
    102. Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo, An experimental survey on big data frameworks, 2018, 86, 0167739X, 546, 10.1016/j.future.2018.04.032
    103. Nicola Pedreschi, Demian Battaglia, Alain Barrat, The temporal rich club phenomenon, 2022, 18, 1745-2473, 931, 10.1038/s41567-022-01634-8
    104. Fragkiskos D. Malliaros, Michalis Vazirgiannis, Vulnerability assessment in social networks under cascade-based node departures, 2015, 110, 0295-5075, 68006, 10.1209/0295-5075/110/68006
    105. Li Zhai, Xiangbin Yan, Guojing Zhang, The bi-directional h-index and B-core decomposition in directed networks, 2019, 531, 03784371, 121715, 10.1016/j.physa.2019.121715
    106. XiaoBing Xiong, Gang Zhou, YongZhong Huang, HaiYong Chen, Ke Xu, Dynamic evolution of collective emotions in social networks: a case study of Sina weibo, 2013, 56, 1674-733X, 1, 10.1007/s11432-013-4892-8
    107. Taylor S. Bolt, Ryan S. Hampton, R. Michael Furr, William Fleeson, Paul J. Laurienti, Dale Dagenbach, 2016, 9780128009352, 51, 10.1016/B978-0-12-800935-2.00003-8
    108. Sabeur Aridhi, Engelbert Mephu Nguifo, Big Graph Mining: Frameworks and Techniques, 2016, 6, 22145796, 1, 10.1016/j.bdr.2016.07.002
    109. Liqin Zhou, Lu An, Zhichao Ba, Zhiyuan Li, 2018, Chapter 30, 978-3-030-03648-5, 301, 10.1007/978-3-030-03649-2_30
    110. Karel Devriendt, Samuel Martin-Gutierrez, Renaud Lambiotte, Variance and Covariance of Distributions on Graphs, 2022, 64, 0036-1445, 343, 10.1137/20M1361328
    111. Francesco Bonchi, Francesco Gullo, Andreas Kaltenbrunner, 2017, Chapter 110176-1, 978-1-4614-7163-9, 1, 10.1007/978-1-4614-7163-9_110176-1
    112. Fedor V. Fomin, Danil Sagunov, Kirill Simonov, Building large k-cores from sparse graphs, 2023, 132, 00220000, 68, 10.1016/j.jcss.2022.10.002
    113. Naga Shailaja Dasari, Ranjan Desh, M. Zubair, 2014, ParK: An efficient algorithm for k-core decomposition on multicore processors, 978-1-4799-5666-1, 9, 10.1109/BigData.2014.7004366
    114. Rakhi Saxena, Sharanjit Kaur, Vasudha Bhatnagar, Social centrality using network hierarchy and community structure, 2018, 32, 1384-5810, 1421, 10.1007/s10618-018-0582-x
    115. X. Zhang, J. Zhu, Skeleton of weighted social network, 2013, 392, 03784371, 1547, 10.1016/j.physa.2012.12.001
    116. Flaviano Morone, Gino Del Ferraro, Hernán A. Makse, The k-core as a predictor of structural collapse in mutualistic ecosystems, 2019, 15, 1745-2473, 95, 10.1038/s41567-018-0304-8
    117. Deming Chu, Fan Zhang, Xuemin Lin, Wenjie Zhang, Ying Zhang, Yinglong Xia, Chenyi Zhang, 2020, Finding the Best k in Core Decomposition: A Time and Space Optimal Solution, 978-1-7281-2903-7, 685, 10.1109/ICDE48307.2020.00065
    118. Conggai Li, Fan Zhang, Ying Zhang, Lu Qin, Wenjie Zhang, Xuemin Lin, Efficient progressive minimum k-core search, 2019, 13, 2150-8097, 362, 10.14778/3368289.3368300
    119. C. Seshadhri, Ali Pinar, Tamara G. Kolda, 2011, An In-depth Study of Stochastic Kronecker Graphs, 978-1-4577-2075-8, 587, 10.1109/ICDM.2011.23
    120. M. Ángeles Serrano, Marián Boguñá, 2022, 9781108865791, 10.1017/9781108865791
    121. Kun Zhao, Xiaomin Ma, Jun Yang, 2011, Efficient Flooding Search: Utilizing K-Scaffold Subgraph, 978-1-4577-1540-2, 1112, 10.1109/ICCIS.2011.141
    122. Teruyoshi Kobayashi, Taro Takaguchi, Alain Barrat, The structured backbone of temporal social ties, 2019, 10, 2041-1723, 10.1038/s41467-018-08160-3
    123. Larissa Statsenko, Vernon Ireland, Alex Gorod, 2016, Self-organising supply networks: A case study of the SA mining industry, 978-1-4673-8727-9, 1, 10.1109/SYSOSE.2016.7542919
    124. Shiyuan Zhou, Yinglin Wang, Service ranking in service networks using parameters in complex networks: a comparative study, 2019, 22, 1386-7857, 2921, 10.1007/s10586-017-1694-6
    125. P. Csermely, A. London, L.-Y. Wu, B. Uzzi, Structure and dynamics of core/periphery networks, 2013, 1, 2051-1310, 93, 10.1093/comnet/cnt016
    126. Marius Eidsaa, Eivind Almaas, s-core network decomposition: A generalization ofk-core analysis to weighted networks, 2013, 88, 1539-3755, 10.1103/PhysRevE.88.062819
    127. Weifeng Pan, Beibei Song, Kangshun Li, Kejun Zhang, Identifying key classes in object-oriented software using generalizedk-core decomposition, 2018, 81, 0167739X, 188, 10.1016/j.future.2017.10.006
    128. Deming Chu, Fan Zhang, Jingjing Lin, 2019, Chapter 21, 978-981-15-1898-0, 297, 10.1007/978-981-15-1899-7_21
    129. Guo-Qing Zhang, Guo-Qiang Zhang, Qing-Feng Yang, Su-Qi Cheng, Tao Zhou, Evolution of the Internet and its cores, 2008, 10, 1367-2630, 123027, 10.1088/1367-2630/10/12/123027
    130. M. Ángeles Serrano, Dmitri Krioukov, Marián Boguñá, Percolation in Self-Similar Networks, 2011, 106, 0031-9007, 10.1103/PhysRevLett.106.048701
    131. Stephen D. Strowes, Colin Perkins, 2012, Harnessing Internet topological stability in Thorup-Zwick compact routing, 978-1-4673-0775-8, 2551, 10.1109/INFCOM.2012.6195651
    132. Chiara Orsini, Enrico Gregori, Luciano Lenzini, Dmitri Krioukov, Evolution of the Internet k-Dense Structure, 2014, 22, 1063-6692, 1769, 10.1109/TNET.2013.2282756
    133. Ricky Laishram, Ahmet Erdem Sariyüce, Tina Eliassi-Rad, Ali Pinar, Sucheta Soundarajan, 2018, Measuring and Improving the Core Resilience of Networks, 9781450356398, 609, 10.1145/3178876.3186127
    134. Xudong Wu, Long Yuan, Xuemin Lin, Shiyu Yang, Wenjie Zhang, 2019, Chapter 36, 978-3-030-18575-6, 604, 10.1007/978-3-030-18576-3_36
    135. Jinshan Wu, Infrastructure of Scientometrics: The Big and Network Picture, 2019, 4, 2543-683X, 1, 10.2478/jdis-2019-0017
    136. Vladimir Batagelj, Matjaž Zaveršnik, Fast algorithms for determining (generalized) core groups in social networks, 2011, 5, 1862-5347, 129, 10.1007/s11634-010-0079-y
    137. Babak Tootoonchi, Venkatesh Srinivasan, Alex Thomo, 2017, Efficient Implementation of Anchored 2-core Algorithm, 9781450349932, 1009, 10.1145/3110025.3120959
    138. Deena R. Schmidt, Roberto F. Galán, Peter J. Thomas, Dirk Gillespie, Stochastic shielding and edge importance for Markov chains with timescale separation, 2018, 14, 1553-7358, e1006206, 10.1371/journal.pcbi.1006206
    139. Pablo Robles, Sebastian Moreno, Jennifer Neville, 2016, Sampling of Attributed Networks from Hierarchical Generative Models, 9781450342322, 1155, 10.1145/2939672.2939808
    140. Ryan J. Gallagher, Jean-Gabriel Young, Brooke Foucault Welles, A clarified typology of core-periphery structure in networks, 2021, 7, 2375-2548, 10.1126/sciadv.abc9800
    141. Konstantinos Angelou, Michael Maragakis, Panos Argyrakis, A structural analysis of the patent citation network by the k-shell decomposition method, 2019, 521, 03784371, 476, 10.1016/j.physa.2019.01.063
    142. Taotao Cai, Shuiqiao Yang, Jianxin Li, Quan Z. Sheng, Jian Yang, Xin Wang, Wei Emma Zhang, Longxiang Gao, Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks, 2022, 1041-4347, 1, 10.1109/TKDE.2022.3199494
    143. Yizong Cheng, Chen Lu, Nan Wang, 2013, Local k-core clustering for gene networks, 978-1-4799-1309-1, 9, 10.1109/BIBM.2013.6732603
    144. Akhlaque Ahmad, Lyuheng Yuan, Da Yan, Guimu Guo, Jieyang Chen, Chengcui Zhang, 2023, Accelerating k-Core Decomposition by a GPU, 979-8-3503-2227-9, 1818, 10.1109/ICDE55515.2023.00142
    145. Wen Bai, Yuncheng Jiang, Yong Tang, Yayang Li, Parallel Core Maintenance of Dynamic Graphs, 2023, 35, 1041-4347, 8919, 10.1109/TKDE.2022.3219096
    146. Sen Gao, Hongchao Qin, Rong-Hua Li, Bingsheng He, Parallel Colorful h -Star Core Maintenance in Dynamic Graphs , 2023, 16, 2150-8097, 2538, 10.14778/3603581.3603593
    147. Fanchen Bu, Geon Lee, Kijung Shin, Hypercore decomposition for non-fragile hyperedges: concepts, algorithms, observations, and applications, 2023, 1384-5810, 10.1007/s10618-023-00956-2
    148. Austin Polanco, M. E. J. Newman, Hierarchical core-periphery structure in networks, 2023, 108, 2470-0045, 10.1103/PhysRevE.108.024311
    149. Marco Mancastroppa, Iacopo Iacopini, Giovanni Petri, Alain Barrat, Hyper-cores promote localization and efficient seeding in higher-order processes, 2023, 14, 2041-1723, 10.1038/s41467-023-41887-2
    150. Esteban Carisimo, Caleb J. Wang, Mia Weaver, Fabián E. Bustamante, Paul Barford, A Hop Away from Everywhere: A View of the Intercontinental Long-haul Infrastructure, 2023, 7, 2476-1249, 1, 10.1145/3626778
    151. Huiping Chen, Alessio Conte, Roberto Grossi, Grigorios Loukides, Solon P. Pissis, Michelle Sweering, On Breaking Truss-Based and Core-Based Communities, 2024, 1556-4681, 10.1145/3644077
    152. Natarajan Meghanathan, Principal components-based quantification of hierarchical k-core assortativity, 2024, 14, 1869-5469, 10.1007/s13278-024-01299-6
    153. Mariane Santos Françoso, Vanessa de Lima Avanci, Alysson Fernandes Mazoni, Green technologies in the knowledge space: Insertion and the moderating role of industry knowledge bases, 2024, 34, 0936-9937, 675, 10.1007/s00191-024-00871-9
    154. Fragkiskos D. Malliaros, Michalis Vazirgiannis, 2013, To stay or not to stay, 9781450322638, 469, 10.1145/2505515.2505561
    155. Andrea Gabrielli, Diego Garlaschelli, Subodh P. Patil, M. Ángeles Serrano, Network renormalization, 2025, 2522-5820, 10.1038/s42254-025-00817-5
    156. Hernán A. Makse, Marta Zava, 2024, Chapter 1, 978-3-031-78057-8, 1, 10.1007/978-3-031-78058-5_1
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