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. 2011 Apr 29;6(4):e18961. doi: 10.1371/journal.pone.0018961

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Lancichinetti et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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We compare three pairs of partitions: the lowest hierarchical partition found by the algorithm (indicated by ) with the set of micro-communities of the benchmark (Fine); the lowest hierarchical partition found by the algorithm with the set of macro-communities of the benchmark (Coarse); the second lowest hierarchical partition found by the algorithm (indicated by ) with the set of macro-communities of the benchmark. The corresponding similarities are plotted as a function of , for fixed . There are vertices, the average degree , the maximum degree , the size of the macro-communities lies between and vertices, the size of the micro-communities lies between and vertices. The exponents of the degree and community size distributions are and .

Inline graphic — We compare three pairs of partitions: the lowest hierarchical partition found by the algorithm (indicated by ) with the set of micro-communities of the benchmark (Fine); the lowest hierarchical partition found by the algorithm with the set of macro-communities of the benchmark (Coarse); the second lowest hierarchical partition found by the algorithm (indicated by ) with the set of macro-communities of the benchmark. The corresponding similarities are plotted as a function of , for fixed . There are vertices, the average degree , the maximum degree , the size of the macro-communities lies between and vertices, the size of the micro-communities lies between and vertices. The exponents of the degree and community size distributions are and .