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CHARACTERIZING CORE-PERIPHERY STRUCTURES IN NETWORKS VIA PRINCIPAL COMPONENT ANALYSIS OF NEIGHBORHOOD-BASED BRIDGE NODE CENTRALITY

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Dr. Abdulrahman O. Nassar ¹ , Dr. Cheng-Hao Lin ¹ ,

⁴ Department of Computer Science, American University in Cairo, Cairo, Egypt

⁴ Institute of Data Science and Engineering, National Tsing Hua University, Hsinchu, Taiwan

Abstract

Complex networks are ubiquitous, modeling interactions in diverse systems from social dynamics to biological processes. A fundamental organizational principle within these networks is the core-periphery structure, where a densely connected core facilitates efficient communication, surrounded by a more loosely connected periphery. Existing methods for detecting this structure often rely on density matrices, spectral properties, or random walks. This article proposes a novel approach that leverages Principal Component Analysis (PCA) applied to the Neighborhood-based Bridge Node Centrality (NBNC) tuple. The NBNC tuple captures a node's local structural importance and its bridging capabilities within the network. By applying PCA, we aim to reduce the dimensionality of these centrality tuples, allowing the most significant structural features related to core-periphery distinction to emerge. This method offers a refined understanding of nodal roles, particularly highlighting the nuanced position of "bridge nodes" at the interface of core and periphery. Through this analytical framework, we demonstrate an effective means to characterize and identify core-periphery components across various real-world networks, providing insights into their robustness, information flow, and overall organization.

Keywords

Core-periphery structure, complex networks, bridge node centrality

References

Barbera, P., Wang, N., Bonneau, R., Jost, J. T., Nagler, J., Tucker, J., Gonzalez Bailon, S. (2015). The Critical Periphery in the Growth of Social Protests. PLoS ONE , 0143611, 1 15. https://doi.org/10.1371/journal.pone.0143611

Batagelj, V., Mrvar, A. (2006). Pajek Datasets . Retrieved from http://vlado.fmf.uni-lj.si/pub/networks/data/

Borgatti, S. P., Everett, M. G. (2000). Models of Core/Periphery Structures. Social Networks , 21 (4), 375 395. https://doi.org/10.1016/S0378-8733(99)00019-2

Cadrillo, A., Gomez Gardenes, J., Zanin, M., Romance, M., Papo, D., Pozo, F., Boccaletti, S. (2013). Emergence of Network Features from Multiplexity. Scientific Reports , 3 (1344), 1 6. https://doi.org/10.1038/srep01344

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to Algorithms (4th ed.) MIT Press.

Cucuringu, M., Rombach, P., Lee, S. H., Porter, M. A. (2016). Detection of Core Periphery Structure in Networks using Spectral Methods and Geodesic Paths. European Journal of Applied Mathematics , 27 , 846 887, 2016. https://doi.org/10.1017/S095679251600022X

De Domenico, M., Sole Ribalta, A., Gomez, S., Areans, A. (2014). Navigability of Interconnected Networks under Random Failures. Proceedings of the National Academy of Sciences , 111 , 8351 8356. https://doi.org/10.1073/pnas.1318469111

Della Rossa, F., Dercole, F., Piccardi, C. (2013). Profiling Core Periphery Network Structure by Random Walkers. Scientific Reports , 3 (1467), 1 8. https://doi.org/10.1038/srep01467

Gallagher, R. J., Young, J. G., Welles, B. F. (2021). A Clarified Typology of Core Periphery Structure in Networks. Science Advances , 7 (12), eabc9800, 1 11. https://doi.org/10.1126/sciadv.abc9800

Geiser, P., & Danon, L. (2003). Community Structure in Jazz. Advances in Complex Systems , 6 ( 563 573. https://doi.org/10.1142/S0219525903001067

Gephi, (2011). Retrieved from https://gephi.org/tutorials/gephi_tutorial_layouts.pdf

Hashler, M., Peienbrock, M., Doran, D. (2019). dbscan: Fast Density based Clustering with R. Journal of Statistical Software , 91 (1), 1 30. https://doi.org/10.18637/jss.v091.i01

Inza, E. P., Vakhania, N., Sigatreta, J. M., Mira, F. A. H. (2023). Exact and Heuristic Algorithms for the Domination Problem. European Journal of Operational Research , 313 (2), 1 30. https:[https://doi.org/10.1016/j.ejor.2023.08.033](https://doi.org/10.1016/j.ejor.2023.08.033)

Jolliffe, I. T. (2002). Principal Component Analysis (1st Ed.), Springer Series in Statistics.

Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., Makse, H. A. (2010). Identification of Influential Spreaders in Complex Networks. Nature Physics , 6 (11), 888 893. https://doi.org/10.1038/nphys1746

Knuth, D. E. (1993). The Stanford GraphBase A Platform for Combinatorial Computing (1st Addison Wesley.

Kojaku, S., Masuda, N. (2017). Finding Multiple Core Periphery Pairs in Networks. Physical Review E , 96 , 052313. https://doi.org/10.1103/PhysRevE.96.052313

Lloyd, S. (1982). Least Squares Quantization in PCM. IEEE Transactions on Information Theory , 28 (2), 129 137. https://doi.org/10.1109/TIT.1982.1056489

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International Journal of Modern Computer Science and IT Innovations

CHARACTERIZING CORE-PERIPHERY STRUCTURES IN NETWORKS VIA PRINCIPAL COMPONENT ANALYSIS OF NEIGHBORHOOD-BASED BRIDGE NODE CENTRALITY

Abstract

Keywords

References

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