<p>Note: <i>w</i>(<i>e<sub>j</sub></i>) refers to the weight of edge <i>e<sub>j</sub></i>, refers to the strength of node ) and <i>randi</i>{1..9} refers to a randomly selected integers between 1 and 9 (inclusive).</p><p>Summary of weighted network experiments.</p
<p>The degree of the central node in both (a) and (b) is 4 and the strength is 12, but the distribut...
Summary statistics of the network with positive edges (positive network) and the network with negati...
In many scientific applications, it is common to use binary (i.e., unweighted) edges in the study of...
<p>(a) Histogram of correlation coefficients (i.e. edge weights) for each group. (b) Schematic diagr...
<p>A) The comparison of number of nodes in M5 to that from random sampling analysis. B) The comparis...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
<p>(Left) An unsampled weighted network consists of nodes, links and weights representing the number...
<p>Because network topologies can be difficult to decipher in large networks, here we illustrate the...
<p>(A) EEG time-series from the 26 scalp electrodes were separately filtered for the delta, theta, a...
A model for the growth of weighted networks is proposed. The model is based on the edge preferential...
The graph combines the single networks of the four time points ti inferred with a dynamic threshold ...
Complex networks grow subject to structural constraints which affect their measurable properties. As...
<p>Summary of results of network analysis: n – number of nodes in the network; L – average shortest ...
The purpose of this paper is to assess the statistical characterization of weighted networks in term...
<p>The degree of the central node in both (a) and (b) is 4 and the strength is 12, but the distribut...
Summary statistics of the network with positive edges (positive network) and the network with negati...
In many scientific applications, it is common to use binary (i.e., unweighted) edges in the study of...
<p>(a) Histogram of correlation coefficients (i.e. edge weights) for each group. (b) Schematic diagr...
<p>A) The comparison of number of nodes in M5 to that from random sampling analysis. B) The comparis...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights a...
<p>(Left) An unsampled weighted network consists of nodes, links and weights representing the number...
<p>Because network topologies can be difficult to decipher in large networks, here we illustrate the...
<p>(A) EEG time-series from the 26 scalp electrodes were separately filtered for the delta, theta, a...
A model for the growth of weighted networks is proposed. The model is based on the edge preferential...
The graph combines the single networks of the four time points ti inferred with a dynamic threshold ...
Complex networks grow subject to structural constraints which affect their measurable properties. As...
<p>Summary of results of network analysis: n – number of nodes in the network; L – average shortest ...
The purpose of this paper is to assess the statistical characterization of weighted networks in term...
<p>The degree of the central node in both (a) and (b) is 4 and the strength is 12, but the distribut...
Summary statistics of the network with positive edges (positive network) and the network with negati...
In many scientific applications, it is common to use binary (i.e., unweighted) edges in the study of...
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