Identifying highly influential nodes in the complicated grief network.
Reads0
Chats0
TLDR
2 indices of a node's expected influence (EI) that account for the presence of negative edges are developed that suggest high-EI nodes, such as emotional pain and feelings of emptiness, may be especially important to the etiology and treatment of CG.Abstract:
The network approach to psychopathology conceptualizes mental disorders as networks of mutually reinforcing nodes (i.e., symptoms). Researchers adopting this approach have suggested that network topology can be used to identify influential nodes, with nodes central to the network having the greatest influence on the development and maintenance of the disorder. However, because commonly used centrality indices do not distinguish between positive and negative edges, they may not adequately assess the nature and strength of a node's influence within the network. To address this limitation, we developed 2 indices of a node's expected influence (EI) that account for the presence of negative edges. To evaluate centrality and EI indices, we simulated single-node interventions on randomly generated networks. In networks with exclusively positive edges, centrality and EI were both strongly associated with observed node influence. In networks with negative edges, EI was more strongly associated with observed influence than was centrality. We then used data from a longitudinal study of bereavement to examine the association between (a) a node's centrality and EI in the complicated grief (CG) network and (b) the strength of association between change in that node and change in the remainder of the CG network from 6- to 18-months postloss. Centrality and EI were both correlated with the strength of the association between node change and network change. Together, these findings suggest high-EI nodes, such as emotional pain and feelings of emptiness, may be especially important to the etiology and treatment of CG. (PsycINFO Database Recordread more
Citations
More filters
Journal ArticleDOI
Can network analysis transform psychopathology
TL;DR: Novel computational methods that may enable researchers to discern the causal structure of disorders (e.g., Bayesian networks) are reviewed, to contrast network analysis with traditional approaches, and consider its strengths and limitations.
Journal ArticleDOI
What Do Centrality Measures Measure in Psychological Networks
Laura F. Bringmann,Timon Elmer,Sacha Epskamp,Robert Krause,David Schoch,Marieke Wichers,Johanna T. W. Wigman,Evelien Snippe +7 more
TL;DR: Critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality, and conclude that betweenness and closness centrality seem especially unsuitable as measures of node importance.
Journal ArticleDOI
Network analysis of depression and anxiety symptom relationships in a psychiatric sample.
Courtney Beard,Alex J. Millner,Marie J. C. Forgeard,Eiko I. Fried,Kean J. Hsu,Michael T. Treadway,Chelsea Leonard,Sarah J. Kertz,Thröstur Björgvinsson +8 more
TL;DR: Exining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.
Journal ArticleDOI
Bridge Centrality: A Network Approach to Understanding Comorbidity.
TL;DR: Four network statistics to identify bridge symptoms are developed: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence, which are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychology networks, and networks outside the field of psychopathology such as social networks.
Journal ArticleDOI
Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples
Eiko I. Fried,Marloes B Eidhof,Sabina Palic,Giulio Costantini,Hilde M. Huisman-van Dijk,Claudi L H Bockting,Iris M. Engelhard,Cherie Armour,Anni Brit Sternhagen Nielsen,Karen-Inge Karstoft +9 more
TL;DR: The importance of future replicability efforts to improve clinical psychological science is discussed and code, model output, and correlation matrices are provided to make the results of this article fully reproducible.
References
More filters
Journal ArticleDOI
Centrality in social networks conceptual clarification
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.
The igraph software package for complex network research
Gábor Csárdi,Tamás Nepusz +1 more
TL;DR: Platform-independent and open source igraph aims to satisfy all the requirements of a graph package while possibly remaining easy to use in interactive mode as well.
Journal ArticleDOI
Sparse inverse covariance estimation with the graphical lasso
TL;DR: Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods.
Journal ArticleDOI
Centrality and network flow
TL;DR: A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes given implicit models of how traffic flows, and that this provides a new and useful way of thinking about centrality.