We review the main results from the literature on the consequences of link and node removal in real social networks. We restrict our review to only those works that adopted the two most common measures of network robustness, i.e., the largest connected component (LCC) and network efficiency (Eff). We consider both binary and weighted network approaches. We show that the study of the response of social networks subjected to link/node removal turns out to be extremely useful for managing a number of real problems. For instance, we show that the consequences of the imposition of social distancing in many states to control the spread of COVID-19 could be analyzed within the framework of social network analysis. Our mini-review outlines that in social networks, it is necessary to consider the weight of links between persons to perform reliable analyses. Finally, we propose promising lines for future research in social network science.

Link and Node Removal in Real Social Networks: A Review / Bellingeri, M.; Bevacqua, D.; Scotognella, F.; Alfieri, R.; Nguyen, Q.; Montepietra, D.; Cassi, D.. - In: FRONTIERS IN PHYSICS. - ISSN 2296-424X. - 8(2020). [10.3389/fphy.2020.00228]

Link and Node Removal in Real Social Networks: A Review

Bellingeri M.;Bevacqua D.;Alfieri R.;Montepietra D.;Cassi D.
2020

Abstract

We review the main results from the literature on the consequences of link and node removal in real social networks. We restrict our review to only those works that adopted the two most common measures of network robustness, i.e., the largest connected component (LCC) and network efficiency (Eff). We consider both binary and weighted network approaches. We show that the study of the response of social networks subjected to link/node removal turns out to be extremely useful for managing a number of real problems. For instance, we show that the consequences of the imposition of social distancing in many states to control the spread of COVID-19 could be analyzed within the framework of social network analysis. Our mini-review outlines that in social networks, it is necessary to consider the weight of links between persons to perform reliable analyses. Finally, we propose promising lines for future research in social network science.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2881699
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