<p>For each network, nodes were divided into 6 bins according to degree: 5 equally-sized bins, and a 6th bin containing the 50 nodes with the highest degree (i.e., the hubs in the hub networks). TPR is shown for each pairing of degree bins (e.g., sixteenth pair </p><p></p><p></p><p><mo stretchy="false">(</mo></p><p><mn>1</mn><mn>6</mn></p><mo stretchy="false">)</mo><p></p><p></p><p></p> refers to edges between the nodes with lowest degrees and the nodes with highest degrees; rightmost pair <p></p><p></p><p><mo stretchy="false">(</mo></p><p><mn>6</mn><mn>6</mn></p><mo stretchy="false">)</mo><p></p><p></p><p></p> refers to edges between the nodes with highest degrees).<p></p
<p>Plot shows average clustering coefficient of nodes per degree for full (left figure) and major (r...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
<p>Initially, all nodes in network <i>A</i> (<i>B</i>) are positive (negative). (a) <i>P</i><sub>+</...
<p>For each network, nodes were divided into 6 bins according to degree: 5 equally-sized bins, and a...
<p>Total number of nodes (<i>N</i>); number of edges (<i>E</i>); average degree per node (〈<i>k</i>〉...
<p>Densities of the true (black) and recovered node degrees of shrinkage (blue), ridge (orange), and...
<p>Plot A and D: Strength distribution of the semantic (A) and users’ interest network (D). The dist...
<p>Thicker edges represent stronger absolute correlations. Left: true network of partial correlation...
<p>Black lines indicate best linear fit to the data (dashed) and model (solid) networks. In panel <i...
<p>(A) Pearson degree correlation coefficient for the model network of size <i>N</i> = 10<sup>5</sup...
<p>The time labels are set to Jun. 2007, Dec. 2007, Jun. 2008, and Dec. 2008 which are interval of s...
<p>(a) Total degree; (b) Average nearest-neighbor degree (ANND); (c) Total strength; (d) Average nea...
Barabási and Albert [1] suggested modeling scale-free networks by the following random graph process...
<p>Because the network is relatively small, the largest degree we get is only 30. Therefore, the res...
<p>The average percent identity is the fraction of links the randomized network had in common with t...
<p>Plot shows average clustering coefficient of nodes per degree for full (left figure) and major (r...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
<p>Initially, all nodes in network <i>A</i> (<i>B</i>) are positive (negative). (a) <i>P</i><sub>+</...
<p>For each network, nodes were divided into 6 bins according to degree: 5 equally-sized bins, and a...
<p>Total number of nodes (<i>N</i>); number of edges (<i>E</i>); average degree per node (〈<i>k</i>〉...
<p>Densities of the true (black) and recovered node degrees of shrinkage (blue), ridge (orange), and...
<p>Plot A and D: Strength distribution of the semantic (A) and users’ interest network (D). The dist...
<p>Thicker edges represent stronger absolute correlations. Left: true network of partial correlation...
<p>Black lines indicate best linear fit to the data (dashed) and model (solid) networks. In panel <i...
<p>(A) Pearson degree correlation coefficient for the model network of size <i>N</i> = 10<sup>5</sup...
<p>The time labels are set to Jun. 2007, Dec. 2007, Jun. 2008, and Dec. 2008 which are interval of s...
<p>(a) Total degree; (b) Average nearest-neighbor degree (ANND); (c) Total strength; (d) Average nea...
Barabási and Albert [1] suggested modeling scale-free networks by the following random graph process...
<p>Because the network is relatively small, the largest degree we get is only 30. Therefore, the res...
<p>The average percent identity is the fraction of links the randomized network had in common with t...
<p>Plot shows average clustering coefficient of nodes per degree for full (left figure) and major (r...
Complex network theory crucially depends on the assumptions made about the degree distribution, whil...
<p>Initially, all nodes in network <i>A</i> (<i>B</i>) are positive (negative). (a) <i>P</i><sub>+</...