Interactive effects of social network centrality and social identification on stress

Abstract

The present study aimed to integrate the social identity approach to health and well‐being with social network analysis. Previous research on the effects of social network centrality on stress has yielded mixed results. Building on the social identity approach, we argued that these mixed results can be explained, in part, by taking into account the degree to which individuals identify with the social network. We hence hypothesized that the effects of social network centrality on stress are moderated by social identification. Using a full roster method, we assessed the social network of first‐year psychology students right after the start of their study programme and three months later. The effects of network centrality (betweenness, closeness, eigenvector centrality) and social identification on stress were examined using structural equation models. As predicted, our results revealed a significant interaction between network centrality and social identification on stress: For weakly or moderately identified students, network centrality was positively related to stress. By contrast, for strongly identified students, network centrality was unrelated to stress. In conclusion, our results point to the perils of being well‐connected yet not feeling like one belongs to a group.

Controlling COVID-19 transmission with isolation of influential nodes

“…In this section, centrality measures such as DC [14] , CC [15] , BC [16] , and EC [12] are proposed to identify the influential and important nodes within a complex or social network. Inverse sum of the shortest distances from a central node to all other nodes denotes closeness centrality.…”

“…It has many functions, such as market advertising, rumor control, disease control, and innovation propagation. As influencers are critical actors for information spread to help the decision-making process, it is necessary to identify influencers in complex networks [10] , [11] , [12] , [13] , [14] , [15] .…”

Controlling COVID-19 Propagation with Quarantine of Influential Nodes

“…Fig. (7) show an SI model for epidemic disease transmission in a population of individuals who are at high-risk.…”

“…1.1. Node ranking using centrality measures Centrality measures such as degree centrality (DC) [4], closeness centrality (CC) [5], betweenness centrality (BC) [6], and eigenvector centrality (EC) [7] have been proposed to identify the influential nodes within a graph and a complex network. Inverse sum of shortest distances from a focal node to all other nodes is define closeness centrality.…”

“…The information flows are essential in order to identify the influential nodes in a network. In the different networks with the different information need to use the different types of centrality measures as pointed by several authors [4][5][6][7].…”