In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson co...

Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to a large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connecte...

We identify three different levels of correlation (pairwise relative ordering, network-wide ranking and prediction through linearity) that could be assessed between a computationally-light centrality metric and a computationally-heavy centrality metric for real-world networks. The Kendall's concordance-based correlation measure could be used to quantitatively assess how well we could consider t...

Assortativity index (A. Index) of real-world network graphs has been traditionally computed based on the degree centrality metric and the networks were classified as assortative, dissortative or neutral if the A. Index values are respectively greater than 0, less than 0 or closer to 0. In this paper, we evaluate the A. Index of real-world network graphs based on some of the commonly used centra...

objective: this paper tries to analyse and visualize the co-authorship network of institutions in 1351 papers published in 7 knowledge and information science journals in iran from 1387 through 1391.methodology: in this paper, we use scientometric and social network analysis to study the collaboration network of institutions. overall structure of the network was investigated using a few macro-l...

a novel algorithm for the fast detection of hubs in chemical networks is presented. thealgorithm identifies a set of nodes in the network as most significant, aimed to be the mosteffective points of distribution for fast, widespread coverage throughout the system. we showthat our hubs have in general greater closeness centrality and betweenness centrality thanvertices with maximal degree, while...

We model the contiguous states (48 states and the District of Columbia) of the United States (US) as an undirected network graph with each state represented as a node and there is an edge between two nodes if the corresponding two states share a common border. We determine a ranking of the states in the US with respect to a suite of node-level metrics: the centrality metrics (degree, eigenvecto...

A novel algorithm for the fast detection of hubs in chemical networks is presented. The algorithm identifies a set of nodes in the network as most significant, aimed to be the most effective points of distribution for fast, widespread coverage throughout the system. We show that our hubs have in general greater closeness centrality and betweenness centrality than vertices with maximal degree, w...

background: co-authorship is one of the most tangible forms of research collaboration. a co-authorship network is a social network in which the authors through participation in one or more publication through an indirect path have linked to each other. the present research using the social network analysis studied co-authorship network of 681 articles published in journal of research in medical...

Robustness estimation is critical for the design and maintenance of resilient networks, one of the global challenges of the 21st century. Existing studies exploit network metrics to generate attack strategies, which simulate intentional attacks in a network, and compute a metric-induced robustness estimation. While some metrics are easy to compute, e.g. degree centrality, other, more accurate, ...