Centrality in social networks conceptual clarification

Freeman, Linton C.

doi:10.1016/0378-8733(78)90021-7

Cited by 13,812 publications

(9,984 citation statements)

References 23 publications

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“…To quantify the importance of each node in the network, we computed centrality indices (Freeman, 1978/1979; Opsahl, Agneessens, & Skvoretz, 2010). Centrality indices reflect how connected a node is within the network and hence how potentially clinically relevant it may be.…”

Section: Methodsmentioning

confidence: 99%

A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

McNally

Heeren

Robinaugh

2017

European Journal of Psychotraumatology

178

214

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Background: The network approach to mental disorders offers a novel framework for conceptualizing posttraumatic stress disorder (PTSD) as a causal system of interacting symptoms. Objective: In this study, we extended this work by estimating the structure of relations among PTSD symptoms in adults reporting personal histories of childhood sexual abuse (CSA; N = 179). Method: We employed two complementary methods. First, using the graphical LASSO, we computed a sparse, regularized partial correlation network revealing associations (edges) between pairs of PTSD symptoms (nodes). Next, using a Bayesian approach, we computed a directed acyclic graph (DAG) to estimate a directed, potentially causal model of the relations among symptoms. Results: For the first network, we found that physiological reactivity to reminders of trauma, dreams about the trauma, and lost of interest in previously enjoyed activities were highly central nodes. However, stability analyses suggest that these findings were unstable across subsets of our sample. The DAG suggests that becoming physiologically reactive and upset in response to reminders of the trauma may be key drivers of other symptoms in adult survivors of CSA. Conclusions: Our study illustrates the strengths and limitations of these network analytic approaches to PTSD.

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Section: Methodsmentioning

confidence: 99%

A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

McNally

Heeren

Robinaugh

2017

European Journal of Psychotraumatology

178

214

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“…For the 6-month one-mode network, the distributions of the three main centrality measurements were computed for each holding type: degree, in-degree (number of different holdings from which a holding receives animals) and betweenness (the frequency at to which a node is on the shortest path between any pair of nodes; Freeman, 1978Freeman, /1979. Moreover, a new measurement proposed by Nö remark et al (2011) was calculated, the ingoing infection chain, which identifies the number of holdings that are connected to one holding taking into account the order of the movements (Nö remark et al, 2011).…”

Section: Network Analysismentioning

confidence: 99%

“…For one-mode networks (Freeman, 1978(Freeman, /1979 In two-mode networks, the degree of a holding is the number of shipment rounds in which it takes part (rate of participation) and the degree of a shipment round reflects the number of holdings where animals were loaded…”

Section: Network Analysismentioning

confidence: 99%

“…The shortest path length or geodesic distance is the smallest number of links represented by animal movement sets required to travel from holding A to holding B (Watts and Strogatz, 1998) The diameter is the longest geodesic distance (Borgatti and Everett, 1997) Distance measurements of two-mode networks with links between rounds and holdings, must be divided by two to be compared to one-mode measurements Low values suggest that epidemic would spread quickly across the network Assortativity Preference of nodes to attach to others that are similar degree (Newman, 2002) Negative assortativity is dissortativity; high-degree nodes tend to attach to low-degree nodes Community Subset of nodes in which there are significantly more links than expected by chance; group of preferentially linked holdings (Newman, 2006) Betweenness Proportion of geodesic distances (represented by animal movement sets) between all pairs of holdings (excluding node A) that pass through node A (Freeman, 1978(Freeman, /1979 …”

Section: Network Analysismentioning

confidence: 99%

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Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Rautureau

Dufour

Durand

2012

Animal

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The networks generated by live animal movements are the principal vector for the propagation of infectious agents between farms, and their topology strongly affects how fast a disease may spread. The structural characteristics of networks may thus provide indicators of network vulnerability to the spread of infectious disease. This study applied social network analysis methods to describe the French swine trade network. Initial analysis involved calculating several parameters to characterize networks and then identifying high-risk subgroups of holdings for different time scales. Holding-specific centrality measurements (‘degree’, ‘betweenness’ and ‘ingoing infection chain’), which summarize the place and the role of holdings in the network, were compared according to the production type. In addition, network components and communities, areas where connectedness is particularly high and could influence the speed and the extent of a disease, were identified and analysed. Dealer holdings stood out because of their high centrality values suggesting that these holdings may control the flow of animals in part of the network. Herds with growing units had higher values for degree and betweenness centrality, representing central positions for both spreading and receiving disease, whereas herds with finishing units had higher values for in-degree and ingoing infection chain centrality values and appeared more vulnerable with many contacts through live animal movements and thus at potentially higher risk for introduction of contagious diseases. This reflects the dynamics of the swine trade with downward movements along the production chain. But, the significant heterogeneity of farms with several production units did not reveal any particular type of production for targeting disease surveillance or control. Besides, no giant strong connected component was observed, the network being rather organized according to communities of small or medium size (<20% of network size). Because of this fragmentation, the swine trade network appeared less structurally vulnerable than ruminant trade networks. This fragmentation is explained by the hierarchical structure, which thus limits the structural vulnerability of the global trade network. However, inside communities, the hierarchical structure of the swine production system would favour the spread of an infectious agent (especially if introduced in breeding herds).

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“…An individual's centrality refers to an individual's position in a social network (Scott, 2000) and is indexed by degree (the number of a member's connections with other network members) and betweenness (the intermediary position of a member who acts like a connecting bridge between some network members or groups). Additionally, an entire social network can be characterized with respect to its "indegree," a measure of the extent to which a few individuals in a group enjoy a central position in members' perceptions of some attribute (Freeman, 1979).…”

Section: Study Purposementioning

confidence: 99%

Effects of the structure of classmates’ perceptions of peers’ academic abilities on children’s perceived cognitive competence, peer acceptance, and engagement

Zhang

2007

Contemporary Educational Psychology

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This study examined the effects of classroom indegree for ability (the degree to which peer nominations as academically capable show high consensus and focus on a relatively few number of children in a classroom) on first grade children's peer acceptance, teacher-rated classroom engagement, and self-perceived cognitive competence. Participants were 291 children located in 84 classrooms. Participating in sociometric interviews were 937 classmates. Consistent with social comparison theory, classroom indegree moderated the associations between children's achievement and classroom engagement and peer liking. Children with lower ability, relative to their classmates, were less accepted by peers and less engaged in classrooms in which students' perceptions of classmates' abilities converged on a relatively few number of students than in classrooms in which peers' perceptions were more dispersed. High indegree was associated with lower self-perceived cognitive competence regardless of ability level.

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Centrality in social networks conceptual clarification

Cited by 13,812 publications

References 23 publications

A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Effects of the structure of classmates’ perceptions of peers’ academic abilities on children’s perceived cognitive competence, peer acceptance, and engagement

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