Link and Node Removal in Real Social Networks: A Review

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.

Structural Vulnerability Analysis of Partially Interdependent Networks: The Joint Influence of Interdependence and Local Worlds

“…In this study, the investigation on network vulnerability against cascading failures is mainly focused on a simple binary networked system model, where links between nodes are only with two states, that is, present or absent. Nonetheless, most realworld networks are naturally weighted with some interaction values associated to the links (i.e., the link weight) [13,44]. Thus, in the following work, the study on the relationships between local-world effect and network attack will be extended to weighted interdependent networks and real-world networks.…”

“…(1) S, relative size of the largest connected component For an unconnected network, considering all the components, that is, the subnetworks of connected nodes, the largest connected component (LCC) represents the component which has the maximum number of connected nodes [44]. Let N ′ and N 0 denote the number of nodes that remain in the LCC and the total number of nodes in the initial undamaged network, respectively.…”

Reference to Global State and Social Contagion Dynamics

“…Probing into this possibility holds promise for enriching our knowledge. Also, whereas our study is based on unweighted networks, most real-world networks are weighted ones [65]. It may non-trivially shift our findings and thereby help identify a new research direction to consider weighted networks.…”

Modeling the Consequences of Social Distancing Over Epidemics Spreading in Complex Social Networks: From Link Removal Analysis to SARS-CoV-2 Prevention

“…When a vaccine is available, one must take into account resource limitations (vaccine doses, doctors, time, costs, etc.) and optimize vaccine administrations [3,4,[8][9][10][11]. This is equivalent to remove some nodes from the network and there is extensive literature about how to rank nodes to be first removed in order to efficiently halt a spreading epidemic [3,4,8].…”