Node centrality in weighted networks: Generalizing degree and shortest paths

By Tore Opsahl, Filip Agneessens, John Skvoretz. Soc. Networks, 2010

3,630 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/68579aa91f57c8a5f8e81d89998d4bb048ec4f93

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Citations in the corpus

Homological scaffolds of brain functional networks (2014, background): “Despite the fact that most network metrics can be extended to the weighted case [9–13], the combined effect of complete connectedness and edge weights makes the interpretation of functional networks significantly harder and motivates the widespread use of ad hoc thresholding methods [7,14–18].”
Deanonymizing mobility traces: using social network as a side-channel (2012, methodology): “For the first step, we identify landmark nodes using a node centrality measure [25, 7], which is a measure of the relative importance of a vertex within a graph.”
Disentangling social networks from spatiotemporal dynamics: the temporal structure of a dolphin society (2012, methodology): “In weighted social networks, the SPL is better described by the lowest sum of the inverse of an edge’s weight (see Opsahl et al. 2010); thus, we used the inverse of the association index (1/HWI) as a measure of the distance between two individuals (the proportion of time that they were…”
Vital nodes identification in complex networks (2016, background): “Since the link weights in the most of the weighted networks are operationalizations of link strength and not the cost of them [282], both Newman [283] and Brandes [284] proposed to adopt the reciprocal of weights to extend closeness centrality and betweenness centrality (more details are shown in th”
Validity Issues in the Use of Social Network Analysis with Digital Trace Data (2011, neutral): “If the link values are binary (unweighted), this is an equivalent approach, but if they are weighted then the function silently performs a weighted centralization function, a much less commonly understood and interpretable measure; see Opsahl et al. (2010) for a discussion of this and alternative me”
A Tutorial on Regularized Partial Correlation Networks (2016, background): “A more extensive overview of these measures and their interpretation is described elsewhere (Costantini et al., 2015a; Epskamp et al., 2017a; Opsahl et al., 2010).”
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity (2018, methodology): “Another class of graph diffusions, whichwe refer to here as social, were used for social and economic models of centrality [3, 4, 14, 18, 29, 32], influence [13, 17, 19, 23], and similarity [12] of nodes.”
Bootstrapped Graph Diffusions (2018, methodology): “Another class of graph diffusions, which we refer to here as social , were used for social and economic models of centrality [3, 4, 14, 18, 30, 34], influence [13, 17, 19, 23], and similarity [12] of nodes.”
Comparing network structures on three aspects: A permutation test. (2022, background): “Overall connectivity can be summarized by global strength and is defined as the weighted absolute sum of all edges in the network (Opsahl et al., 2010).”
Qgraph: Network visualizations of relationships in psychometric data (2012, positive): “…new to R , while at the same time oﬀering more advanced customization options for experienced R users. qgraph is not designed for numerical analysis of graphs (Boccaletti et al. 2006), but can be used to compute the node centrality measures of weighted graphs proposed by Opsahl et al. (2010).”
Estimating psychological networks and their accuracy: A tutorial paper (2016, background): “A typical way of assessing the importance of nodes in this network is to compute centrality indices of the network structure (Costantini et al., 2015a; Newman, 2010; Opsahl et al., 2010).”
Bridge Centrality: A Network Approach to Understanding Comorbidity (2019, methodology): “For weighted graphs, these packages rely on the node centrality formulas outlined by Opsahl, Agneessens, and Skvoretz (2010).”
State of the aRt personality research: A tutorial on network analysis of personality data in R (2015, neutral): “4 Degree centrality can be also easily extended to directed networks, by considering separately the number of incoming ties ( indegree ) and the number of outgoing ties ( outdegree ). inverse value (Brandes, 2008; Opsahl, Agneessens, & Skvoretz, 2010).”