Clustering coefficient is an important measure in social network analysis, community detection and many other applications. However, it is expensive to compute clustering coefficient for the real-world networks, because many networks, such as Facebook and Twitter, are usually large and evolving continuously. Aiming to improve the performance of clustering coefficient computation for the large a...

An important source of high clustering coefficient in real-world networks is transitivity. However, existing approaches for modeling transitivity suffer from at least one of the following problems: i) they produce graphs from a specific class like bipartite graphs, ii) they do not give an analytical argument for the high clustering coefficient of the model, and iii) their clustering coefficient...

One of the main functionalities of capital market is to enhance liquidity in the market. Mutual funds are modern financial institutions which are designed with the aim of absorbing funds from investors and devote them to buy a variety of securities in order to reduce investment risks, exploit the economies of scale and finally make a reasonable return for investors. Regarding effective role of ...

‎The global FOM and GMRES algorithms are among the effective‎ ‎methods to solve Sylvester matrix equations‎. ‎In this paper‎, ‎we‎ ‎study these algorithms in the case that the coefficient matrices‎ ‎are real symmetric (real symmetric positive definite) and extract‎ ‎two CG-type algorithms for solving generalized Sylvester matrix‎ ‎equations‎. ‎The proposed methods are iterative projection metho...

The clustering coefficient quantifies how well connected are the neighbors of a vertex in a graph. In real networks it decreases with the vertex degree, which has been taken as a signature of the network hierarchical structure. Here we show that this signature of hierarchical structure is a consequence of degree-correlation biases in the clustering coefficient definition. We introduce a definit...

The congruence coefficient was first introduced by Burt (1948) under the name of unadjusted correlation as a measure of the similarity of two factorial configurations. The name congruence coefficient was later tailored by Tucker (1951, see also Harman, 1976). The congruence coefficient is also sometimes called a monotonicity coefficient (Borg & Groenen, 1997, p. 203). The congruence coefficient...

We introduce a minimal model of growing small-world network generated by attaching to edges. The produced network is a plane graph which exists in real-life world. We obtain the analytic results of degree distribution decaying exponentially with degree and average clustering coefficient C = 3 2 ln3− 1 ≈ 0.6479, which are in good agreement with the numerical simulations. We also prove that the i...

The congruence between two configurations of points quantifies their similarity. The configurations to be compared are, in general, produced by factor analytic methods which decompose an “observations by variables” data matrix and produce one set of factor scores for the observations and one set of factor scores (i.e., the “loadings”) for the variables. The congruence between two sets of factor...