Karate network (34 nodes/2 classes).

<p>The transform matrix (<b>A</b>) and the dendrogram (<b>B</b>) obtained by ELC, the transform matrix (<b>C</b>) and the dendrogram (<b>D</b>) obtained by LC. (<b>E–G</b>) Communities and corresponding values of Extended Quality of modularity (EQ), Partition Density (PD), In-Group-Proportion (IGP), Communities Number (CN), Cover Rate (CR) and number of Uncovered Nodes (UN) obtained by ELC, LC and CPM. *the red and bold data marked with an asterisk (*) is the best value of each evaluation on the dataset for the three methods.</p

Dolphin network (62 nodes/2 classes).

<p>The transform matrix <b>(A)</b> and the dendrogram (<b>B)</b> obtained by ELC, the transform matrix <b>(C)</b> and the dendrogram <b>(D)</b> obtained by LC. <b>(E-G)</b> Communities and corresponding values of Extended Quality of modularity (EQ), Partition Density (PD), In-Group-Proportion (IGP), Communities Number (CN), Cover Rate (CR) and number of Uncovered Nodes (UN) obtained by ELC, LC and CPM. *the red and bold data marked with an asterisk (*) is the best value of each evaluation on the dataset for the three methods.</p

Comparison with three methods on five real-world networks by cover rate and uncovered nodes.

*<p>the bold data marked with an asterisk (*) is the best value of each evaluation on the dataset for three methods.</p>**<p>CR: Cover Rate; UN: number of Uncovered Nodes.</p

A selected artificial network set with different node average degrees and <b><i>p_inside</i></b><b> values.</b>

<p>A selected artificial network set with different node average degrees and <b><i>p_inside</i></b><b> values.</b></p

Football network (115 nodes/12 classes).

<p>The transform matrix (<b>A</b>) and the dendrogram (<b>B</b>) obtained by ELC, the transform matrix (<b>C</b>) and the dendrogram (<b>D</b>) obtained by LC. (<b>E–G</b>) Communities and corresponding values of Extended Quality of modularity (EQ), Partition Density (PD), In-Group-Proportion (IGP), Communities Number (CN), Cover Rate (CR) and number of Uncovered Nodes (UN) obtained by ELC, LC and CPM. *the red and bold data marked with an asterisk (*) is the best value of each evaluation on the dataset for the three methods.</p

LC performance on different artificial datasets conditions.

*<p>the bold data marked with an asterisk (*) is the best value with the same location in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066005#pone-0066005-t002" target="_blank">Tables 2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066005#pone-0066005-t004" target="_blank">4</a>.</p>**<p>EQ: Extended Quality of modularity; IGP: In-Group-Proportion; PD: Partition Density; CN: Communities Number.</p><p>To avoid accidental influence of single artificial network, all types of evaluation values are average values of 10 networks in each condition.</p

CPM performance on different artificial datasets conditions.

*<p>the bold data marked with an asterisk (*) is the best value with the same location in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066005#pone-0066005-t002" target="_blank">Tables 2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066005#pone-0066005-t004" target="_blank">4</a>.</p>**<p>EQ: Extended Quality of modularity; IGP: In-Group-Proportion; PD: Partition Density; CN: Communities Number.</p><p>To avoid accidental influence of single artificial network, all types of evaluation values are average values of 10 networks in each condition.</p

Y2H network (1647 nodes/3 sources).

<p>The transform matrix (<b>A</b>) and the dendrogram (<b>B</b>) obtained by ELC, the transform matrix (<b>C</b>) and the dendrogram (<b>D</b>) obtained by LC. (<b>E–G</b>) Communities and corresponding values of Extended Quality of modularity (EQ), Partition Density (PD), In-Group-Proportion (IGP), Communities Number (CN), Cover Rate (CR) and number of Uncovered Nodes (UN) obtained by ELC, LC and CPM. *the red and bold data marked with an asterisk (*) is the best value of each evaluation on the dataset for the three methods.</p

A simple network for ELC and LC calculation.

<p>(<b>A</b>) A simple network example mentioned in Ahn’s paper (2010). (<b>B</b>) The transform matrix and (<b>C</b>) The dendrogram obtained by ELC on (A)’s example networks. (<b>D</b>) The transform matrix and (<b>E</b>) the dendrogram obtained by LC on (A)’s example networks.</p

ELC performance on different artificial datasets conditions.

*<p>the bold data marked with an asterisk (*) is the best value with the same location in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066005#pone-0066005-t002" target="_blank">Tables 2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066005#pone-0066005-t004" target="_blank">4</a>.</p>**<p>EQ: Extended Quality of modularity; IGP: In-Group-Proportion; PD: Partition Density; CN: Communities Number.</p><p>To avoid accidental influence of single artificial network, all types of evaluation values are average values of 10 networks in each condition.</p