Dynamic functional connectivity correlates of mental workload
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Closeness
Katz centrality
Social Network Analysis
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Background: In network science, although different types of centrality measures have been introduced to determine important nodes of networks, a consensus pipeline to select and implement the best-tailored measure for each complex network is still an open field. In the present study, we examine the node centrality profiles of protein-protein interaction networks (PPINs) in order to detect which measure is succeeding to predict influential proteins. We study and demonstrate the effects of inherent topological features and network reconstruction approaches on the centrality measure values. Results: PPINs were used to compare a large number of well-known centrality measures. Unsupervised machine learning approaches, including principal component analysis (PCA) and clustering methods, were applied to find out how these measures are similar in terms of characterizing and assorting network influential constituents. We found that the principle components of the network centralities and the contribution level of them demonstrated a network-dependent significance of these measures. We show that some centralities namely Latora, Decay, Lin, Freeman, Diffusion, Residual and Average had a high level of information in comparison with other measures in all PPINs. Finally, using clustering analysis, we restated that the determination of important nodes within a network depends on its topology. Conclusions: Using PCA and identifying the contribution proportion of the variables, i.e., centrality measures in principal components, is a prerequisite step of network analysis in order to infer any functional consequences, e.g., the essentiality of a node. Our conclusion is based on the signal and noise modeling using PCA and the similarity distance between clusters. Also, an interesting strong correlation between silhouette criterion and contribution value was found which corroborates our results.
Network Analysis
Katz centrality
Network theory
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Centrality measures are extremely important in the analysis of social networks, with applications such as the identification of the most influential individuals for effective target marketing. Eigenvector centrality and PageRank are among the most useful centrality measures, but computing these measures can be prohibitively expensive for large social networks. This paper explores multiple approaches to improve the computational effort required to compute relative centrality measures. First, we show that small neural networks can be effective in fast estimation of the relative ordering of vertices in a social network based on these centrality measures. Then, we show how network sampling can be used to reduce the running times for calculating the ordering of vertices; degree centrality-based sampling reduces the running time of the key node identification problem. Finally, we propose the approach of incremental updating of centrality measures in dynamic networks.
PageRank
Identification
Katz centrality
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Network theory
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Closeness centrality is a fundamental centrality measure that quantifies how centrally located a node is, within a network, based on its total distances to all other nodes. In this paper, we first derive a set of linear inequality and equality constraints, which are distributed in nature, that characterize closeness centrality in lieu of its original definition. We then use these constraints to develop a scalable distributed algorithm, which enables nodes in a network to cooperatively estimate their individual closeness with only local interaction and without any centralized coordination, nor high memory usages. Finally, we evaluate the algorithm performance via extensive simulation, showing that it yields closeness estimates that are 91% accurate in terms of ordering, on random geometric, Erdös-Rényi, and Barabási-Albert graphs.
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Firstly introduced in social science, the notion of centrality has spread to the whole complex network science. A centrality is a measure that quanti es whether an element of a network is well served or not, easy to reach, necessary to cross. This article focuses on cities' street network (seen
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Complex networks represent one of the corner stones and play a central role in several Computer Science domains. Research in these networks represents a multidisciplinary approach due to the requirements to implement the statistical mechanics with graph theory and other techniques. The key property in the complex networks are their centrality measures. Network centrality is having a high impact on the network behaviors, dynamicity, and information spreading can deliver significant information about its organizations. Several metrics are developed to estimate the node centrality in complex networks. Each node centrality measure reflects its topological importance in the network among others. Adjacency matrix is used to derive and perform all the centrality measures based on several mathematical computations. Most of these measures may behave similarly in their statistical analyses. So some of these measures can be considered as redundant due to these and their complexity. This study tries to investigate the correlation between any pair of six selected centrality measures. This approach may advise to use the strongly correlated low-complexity metric as an approximation instead of the high complexity one. To perform this study a correlation analysis study is implemented on 6 estimated centrality measures for three different datasets. The alternate measures are selected according to their correlation coefficients strengths.
Adjacency matrix
Katz centrality
Network theory
Network Analysis
Network controllability
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Firstly introduced in social science, the notion of centrality has spread to the whole complex network science. A centrality is a measure that quantifies whether an element of a network is well served or not, easy to reach, necessary to cross. This article focuses on cities' street network (seen as a communication network). We redefine two classical centralities (the closeness and the straightness) and introduce the notion of simplest centrality. To this we introduce a mathematical framework which allows considering a city as a geometrical continuum rather than a plain topological graph. The color plotting of the various centralities permits a visual analysis of the city and to diagnose local malfunctionings. The relevance of our framework and centralities is discussed from visual analysis of French towns and from computational complexity.
Katz centrality
Closeness
Network theory
Network Analysis
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Centrality is an important measure to identify the most important actors in a network. This paper discusses the various Centrality Measures used in Social Network Analysis. These measures are tested on complex real-world social network data sets such as Video Sharing Networks, Social Interaction Network and Co-Authorship Networks to examine their effects on them. We carry out the correlation analysis of these centralities and plot the results to recommend when to use those centrality measures. Additionally, we introduce a new centrality measure - Cohesion Centrality based on the cohesiveness of a graph, develop its sequential algorithm and further devise a parallel algorithm to implement it.
Katz centrality
Cohesion (chemistry)
Group cohesiveness
Social Network Analysis
Network Analysis
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Network Analysis
Social Network Analysis
Network theory
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The purpose of this paper is to implement the theoretical closeness centrality measurement algorithm [1] that was proposed by the authors' research group in order to numerically analyze closeness centrality measures among workflow-performers on a workflow-supported social network model. We implement the essential part of the proposed algorithm[l], which is a closeness centrality analysis equation. Finally, we illustrate the implemented algorithm by showing its run-time screen-shots with an operational example.
Closeness
Katz centrality
Social Network Analysis
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