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Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis

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  • Natarajan Meghanathan

Abstract

We seek to quantify the extent of similarity among nodes in a complex network with respect to two or more node-level metrics (like centrality metrics). In this pursuit, we propose the following unit disk graph-based approach: we first normalize the values for the node-level metrics (using the sum of the squares approach) and construct a unit disk graph of the network in a coordinate system based on the normalized values of the node-level metrics. There exists an edge between two vertices in the unit disk graph if the Euclidean distance between the two vertices in the normalized coordinate system is within a threshold value (ranging from 0 to , where k is the number of node-level metrics considered). We run a binary search algorithm to determine the minimum value for the threshold distance that would yield a connected unit disk graph of the vertices. We refer to “1 − (minimum threshold distance ) †as the node similarity index (NSI; ranging from 0 to 1) for the complex network with respect to the k node-level metrics considered. We evaluate the NSI values for a suite of 60 real-world networks with respect to both neighborhood-based centrality metrics (degree centrality and eigenvector centrality) and shortest path-based centrality metrics (betweenness centrality and closeness centrality).

Suggested Citation

  • Natarajan Meghanathan, 2019. "Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis," Complexity, Hindawi, vol. 2019, pages 1-22, March.
  • Handle: RePEc:hin:complx:6871874
    DOI: 10.1155/2019/6871874
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    1. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    2. Seierstad, Cathrine & Opsahl, Tore, 2011. "For the few not the many? The effects of affirmative action on presence, prominence, and social capital of women directors in Norway," Scandinavian Journal of Management, Elsevier, vol. 27(1), pages 44-54, March.
    3. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    4. L. Šubelj & M. Bajec, 2011. "Robust network community detection using balanced propagation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 81(3), pages 353-362, June.
    5. Cong Li & Qian Li & Piet Mieghem & H. Stanley & Huijuan Wang, 2015. "Correlation between centrality metrics and their application to the opinion model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(3), pages 1-13, March.
    6. Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
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