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Multicores-periphery structure in networks

Published online by Cambridge University Press:  25 April 2019

Bowen Yan  and
Jianxi Luo
Bowen Yan*
Affiliation:
Engineering Product Development Pillar & SUTD-MIT International Design Centre, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore
Jianxi Luo
Affiliation:
Engineering Product Development Pillar & SUTD-MIT International Design Centre, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore
*
*Corresponding author. Email: bowen_yan@sutd.edu.sg
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Abstract

Many real-world networks exhibit a multicores-periphery structure, with densely connected vertices in multiple cores surrounded by a general periphery of sparsely connected vertices. Identification of the multicores-periphery structure can provide a new lens to understand the structures and functions of various real-world networks. This paper defines the multicores-periphery structure and introduces an algorithm to identify the optimal partition of multiple cores and the periphery in general networks. We demonstrate the performance of our algorithm by applying it to a well-known social network and a patent technology network, which are best characterized by the multicores-periphery structure. The analyses also reveal the differences between our multicores-periphery detection algorithm and two state-of-the-art algorithms for detecting the single core-periphery structure and community structure.

Keywords

mesoscale structurecore-periphery structurecommunity detection

Information

Type
Original Article
Information
Network Science , Volume 7 , Issue 1 , March 2019 , pp. 70 - 87
Copyright
© Cambridge University Press 2019 

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References

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.CrossRefGoogle Scholar
Borgatti, S. P., & Everett, M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.Google Scholar
Brandes, U., & Erlebach, T. (2005). Network analysis: Methodological foundations (Vol. 3418). Berlin, Heidelberg: Springer Science & Business Media.CrossRefGoogle Scholar
Bruckner, S., Hüffner, F., & Komusiewicz, C. (2015). A graph modification approach for finding core-periphery structures in protein interaction networks. Algorithms for Molecular Biology, 10(1), 16.CrossRefGoogle ScholarPubMed
Csermely, P. (2018). The wisdom of networks: A general adaptation and learning mechanism of complex systems: The network core triggers fast responses to known stimuli; innovations require the slow network periphery and are encoded by core-remodeling. BioEssays, 40(1), 1700150.CrossRefGoogle Scholar
Csermely, P., London, A., Wu, L.-Y., & Uzzi, B. (2013). Structure and dynamics of core/periphery networks. Journal of Complex Networks, 1(2), 93–123.CrossRefGoogle Scholar
Della Rossa, F., Dercole, F., & Piccardi, C. (2013). Profiling core-periphery network structure by random walkers. Scientific Reports, 3.CrossRefGoogle Scholar
Everett, M. G., & Borgatti, S. P. (2000). Peripheries of cohesive subsets. Social Networks, 21(4), 397–407.CrossRefGoogle Scholar
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3), 75–174.CrossRefGoogle Scholar
Hidalgo, C. A., Klinger, B., Barabasi, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482–487. doi: 10.1126/science.1144581.CrossRefGoogle ScholarPubMed
Holme, P. (2005). Core-periphery organization of complex networks. Physical Review E, 72(4), 046111.Google ScholarPubMed
Jaccard, P. (1901). Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 241–272.Google Scholar
Liu, Y., Tang, M., Zhou, T., & Do, Y. (2015). Improving the accuracy of the k-shell method by removing redundant links: From a perspective of spreading dynamics. Scientific Reports, 5, 13172.CrossRefGoogle ScholarPubMed
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.CrossRefGoogle Scholar
Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69(6), 066133.CrossRefGoogle ScholarPubMed
Pan, R. K., Sinha, S., Kaski, K., & Saramäki, J. (2012). The evolution of interdisciplinarity in physics research. Scientific Reports, 2, 551.CrossRefGoogle ScholarPubMed
Rombach, M. P., Porter, M. A., Fowler, J. H., & Mucha, P. J. (2014). Core-periphery structure in networks. SIAM Journal on Applied Mathematics, 74(1), 167–190.CrossRefGoogle Scholar
Shanahan, M., & Wildie, M. (2012). Knotty-centrality: Finding the connective core of a complex network. PLoS One, 7(5), e36579.Google ScholarPubMed
Silva, M. R. D., Ma, H., & Zeng, A.-P. (2008). Centrality, network capacity, and modularity as parameters to analyze the core-periphery structure in metabolic networks. Proceedings of the IEEE, 96(8), 1411–1420.Google Scholar
Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.Google ScholarPubMed
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8, pp. 250): Cambridge University Press.CrossRefGoogle Scholar
Xu, J. J., & Chen, H. (2005). CrimeNet explorer: A framework for criminal network knowledge discovery. ACM Transactions on Information Systems (TOIS), 23(2), 201–226.Google Scholar
Yan, B., & Luo, J. (2016). Measuring technological distance for patent mapping. Journal of the Association for Information Science and Technology. doi: 10.1002/asi.23664.CrossRefGoogle Scholar
Yan, B., & Luo, J. (2017). Measuring technological distance for patent mapping. Journal of the Association for Information Science and Technology, 68(2), 423–437.CrossRefGoogle Scholar
Yang, J., & Leskovec, J. (2014). Overlapping communities explain core-periphery organization of networks. Proceedings of the IEEE, 102(12), 1892–1902.Google Scholar
Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4), 452–473.CrossRefGoogle Scholar
Zhang, X., Martin, T., & Newman, M. (2015). Identification of core-periphery structure in networks. Physical Review E, 91(3), 032803.CrossRefGoogle ScholarPubMed