In this paper we propose a clustering procedure aimed at grouping time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms w...

We propose an extended local-world evolving network model including a triad formation step. In the process of network evolution, random fluctuation in the number of new edges is involved. We derive analytical expressions for degree distribution, clustering coefficient and average path length. Our model can unify the generic properties of real-life networks: scale-free degree distribution, high ...

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small-world effect. While the average shortest path length increases logarithmically as in random networks, the clustering coefficient assumes a large value independent of system size. We derive analytical expressions for the clu...

In this paper, a new local-world evolving network model including triad formation mechanism is proposed and studied. Analytical expressions for degree distribution and clustering coefficient are derived using continuum theory. It is shown that the degree distribution transfers from exponential to power-law scaling, and the clustering coefficient is tunable with known parameters. Finally, the nu...

Small-worlds represent efficient communication networks that obey two distinguishing characteristics: a high clustering coefficient together with a small characteristic path length. Evoking small-world properties in spatially organized networks, like ad hoc and sensor networks, is the objective of Reckful Roaming, as introduced in this paper. Reckful Roaming is a 2-localized algorithm that sele...

This paper presents a time-series whole clustering system that incrementally constructs a hierarchy of clusters. The Online DivisiveAgglomerative Clustering (ODAC) system is an incremental implementation of divisive analysis clustering, using the correlation between timeseries as similarity measure. The system tests existing clusters by descending order of diameters, looking for a possible bina...

In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and BarabásiAlbert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m 9 m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity,...

We develop a network in which the natural numbers are the vertices. The decomposition of natural numbers by prime numbers is used to establish the connections. We perform data collapse and show that the degree distribution of these networks scales linearly with the number of vertices. We explore the families of vertices in connection with prime numbers decomposition. We compare the average dist...

There are many researchers proposed social network models in recent years, and most of them focus on clustering coefficient property of a small-world network and power law degree distribution of a scale-free property. In social network topology, we observed the network is consisted of many nodes with small connectivity and a few high-degree nodes. In the small connectivity part, there are many ...

The purpose of this paper is to assess the statistical characterization of weighted networks in terms of the generalization of the relevant parameters, namely, average path length, degree distribution, and clustering coefficient. Although the degree distribution and the average path length admit straightforward generalizations, for the clustering coefficient several different definitions have b...