Some unique properties of eigenvector centrality

By Phillip Bonacich. Soc. Networks, 2007

1,529 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a7b66df4aa6dfac4c513c9c32e8dd6cf1720accb

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Citations in the corpus

Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks (2008, background): “This will be particularly important in networks whose node eigencentrality is not fully captured by the degree centrality [62], e.”
The structural changes of a local tourism network: comparison of before and after COVID-19 (2021, background): “CB(ni) = ∑ j, k gjk(ni) gjk (3) Eigenvector centrality indicates that themore connections a node has, the higher its centrality, and it is used to measure the centrality of a node within the network through eigenvalues (Bonacich, 2007).”
Study on centrality measures in social networks: a survey (2018, background): “Bonacich (2007) proposed some unique theory of eigenvector centrality.”
State of the aRt personality research: A tutorial on network analysis of personality data in R (2015, neutral): “Among the most prominent of those indices are eigenvector centrality (Bonacich, 1972, 2007), Bonacich power (Bonacich, 1987) and alpha centrality (Bonacich & Lloyd, 2001).”
Vital nodes identification in complex networks (2016, background): “The centrality of a node is proportional to the summation of the centralities of the nodes to which it is connected [95].”
Social Networks and Causal Inference (2013, methodology): “…that have been proposed in the literature to choose central subjects in a group/network according to the integrative criterion, including the indegree centrality method (Valente and Pumpuang 2007), the eigenvector centrality method (Bonacich 2007), and the key players method (Borgatti 2006).”
Modeling systemic risk with Markov Switching Graphical SUR models (2019, background): “First we show that our weighted eigenvector centrality measure is the limit of the rescaled Bonacich’s c(β)centrality (see Bonacich 2007) of the graph G̃k of regime k ∈ K.”
Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain (2010, methodology): “Eigenvector centrality Eigenvector centrality was first introduced by Bonacich [11,12] and a later variant of it is a central part of Google’s PageRank algorithm [14].”
Statistical physics of vaccination (2016, background): “Under many circumstances, a node’s importance in one network increases via building connections to other nodes that are themselves important, which is the concept behind eigenvector centrality [297, 316, 318, 319].”
A comparative network analysis of social style in macaques (2011, background): “Individual eigenvector centrality coefficient (Newman 2004; Bonacich 2007): the connection degree of an individual within its group.”
Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering (2013, background): “A number of centrality indices have been proposed to address this problem, such as degree centrality, closeness centrality [9], betweenness centrality [10], and eigenvector centrality [11].”
Current state and future trends: a citation network analysis of the learning analytics field (2014, methodology): “The three networks were analyzed using the following well-established measures in social network analysis [3; 4; 7; 22]:”